var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"host\":\"bibbase.org\"},\n      data: [{\"title\":\"Using hidden multi-state Markov models with multi-parameter volcanic data to provide empirical evidence for alert level decision-support\",\"type\":\"article\",\"year\":\"2006\",\"keywords\":\"Bayesian Belief Network,eruption forecasting,evidence science,hidden Markov model,volcanology\",\"pages\":\"112-124\",\"volume\":\"153\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0377027305003872\",\"month\":\"5\",\"id\":\"712032b5-7ec0-3ca8-93e3-be39f0fb306a\",\"created\":\"2015-04-12T18:30:34.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"For the purposes of eruption forecasting and hazard mitigation, a volcanic crisis may be represented as a staged progression of states of unrest, each with its own timescale and likelihood of transition to other states (or to climactic eruption). If the state conditions can be interpreted physically, e.g., in terms of advancing materials failure, this knowledge could be used directly to inform decisions on alert level setting. A multi-state Markov process provides one simple model for defining states and for estimating rates of switching between states. However, for eruptive processes, such states are not directly observable and must be inferred from latent markers, such as seismic activity, gas output, deformation rates, etc., some of which may be contradictory. Interpretations of uncertain data will be liable to error, so a model is needed which can simultaneously estimate both elements: the transition likelihood of a hidden process and the probabilities of state misclassification. We describe the concept and underlying principles of continuous-time hidden Markov models and demonstrate them in a decision-support context with a preliminary working implementation using MULTIMO data. Where multi-parameter streams of raw, processed or conditioned data of different kinds are available, these can be input in near real-time to appropriate hidden multi-state Markov models, the outputs of each providing their own objective analyses of eruptive state in probabilistic terms. These separate, multiple indicators of state can then be input into a Bayesian Belief Network framework for weighing and combining them as different strands of evidence, together with other observations, data, interpretations and expert judgment.\",\"bibtype\":\"article\",\"author\":\"Aspinall, W.P. and Carniel, R. and Jaquet, O. and Woo, G. and Hincks, T.\",\"doi\":\"10.1016/j.jvolgeores.2005.08.010\",\"journal\":\"Journal of Volcanology and Geothermal Research\",\"number\":\"1-2\",\"bibtex\":\"@article{\\n title = {Using hidden multi-state Markov models with multi-parameter volcanic data to provide empirical evidence for alert level decision-support},\\n type = {article},\\n year = {2006},\\n keywords = {Bayesian Belief Network,eruption forecasting,evidence science,hidden Markov model,volcanology},\\n pages = {112-124},\\n volume = {153},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0377027305003872},\\n month = {5},\\n id = {712032b5-7ec0-3ca8-93e3-be39f0fb306a},\\n created = {2015-04-12T18:30:34.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {For the purposes of eruption forecasting and hazard mitigation, a volcanic crisis may be represented as a staged progression of states of unrest, each with its own timescale and likelihood of transition to other states (or to climactic eruption). If the state conditions can be interpreted physically, e.g., in terms of advancing materials failure, this knowledge could be used directly to inform decisions on alert level setting. A multi-state Markov process provides one simple model for defining states and for estimating rates of switching between states. However, for eruptive processes, such states are not directly observable and must be inferred from latent markers, such as seismic activity, gas output, deformation rates, etc., some of which may be contradictory. Interpretations of uncertain data will be liable to error, so a model is needed which can simultaneously estimate both elements: the transition likelihood of a hidden process and the probabilities of state misclassification. We describe the concept and underlying principles of continuous-time hidden Markov models and demonstrate them in a decision-support context with a preliminary working implementation using MULTIMO data. Where multi-parameter streams of raw, processed or conditioned data of different kinds are available, these can be input in near real-time to appropriate hidden multi-state Markov models, the outputs of each providing their own objective analyses of eruptive state in probabilistic terms. These separate, multiple indicators of state can then be input into a Bayesian Belief Network framework for weighing and combining them as different strands of evidence, together with other observations, data, interpretations and expert judgment.},\\n bibtype = {article},\\n author = {Aspinall, W.P. and Carniel, R. and Jaquet, O. and Woo, G. and Hincks, T.},\\n doi = {10.1016/j.jvolgeores.2005.08.010},\\n journal = {Journal of Volcanology and Geothermal Research},\\n number = {1-2}\\n}\",\"author_short\":[\"Aspinall,  W.\",\"Carniel,  R.\",\"Jaquet,  O.\",\"Woo,  G.\",\"Hincks,  T.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0377027305003872\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"aspinall-carniel-jaquet-woo-hincks-usinghiddenmultistatemarkovmodelswithmultiparametervolcanicdatatoprovideempiricalevidenceforalertleveldecisionsupport-2006\",\"role\":\"author\",\"keyword\":[\"Bayesian Belief Network\",\"eruption forecasting\",\"evidence science\",\"hidden Markov model\",\"volcanology\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Application of Bayesian in determining productive zones by well log data in oil wells\",\"type\":\"article\",\"year\":\"2012\",\"keywords\":\"Bayesian Network,net pay,petrophysics,productive zone,well test\",\"pages\":\"47-54\",\"volume\":\"94-95\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0920410512001702\",\"month\":\"9\",\"id\":\"5e0764ec-d115-302e-b597-ef9875329209\",\"created\":\"2015-04-12T18:30:34.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Exploration specialists conventionally utilize a cut-off-based method to find productive zones inside the oil wells. Using conventional method, pay zones are separated crisply from non-pay zones by applying cut-off values on some petrophysical features. In this paper, a Bayesian technique is developed to find productive zones (net pays), and Bayesian Network is used to select the most appropriate input features for this newly developed method. So, two Bayesian methods were developed: the first one with conventional pay determination inputs (shale percent, porosity and water saturation), the other with two inputs, selected by Bayesian Network (porosity and water saturation). Two developed Bayesian methods are applied on well log dataset of two wells: one well is dedicated for training and testing Bayesian methods, the other for checking generalization ability of the proposed methods. Outputs of two presented methods were compared with the results of conventional cut-off-based method and production test results (i.e. a direct procedure to check validation of proposed methods). Results show that the most prominent advantage of developed Bayesian method is determination of net pays fuzzily with no need to identify cut-offs, in addition to higher precision of classification: nearly 30% improvement in precision of determining net pays of first well (training well), and about 50% improvement in precision of determining productive zones through the generalizing well.\",\"bibtype\":\"article\",\"author\":\"Masoudi, Pedram and Tokhmechi, Behzad and Ansari Jafari, Majid and Zamanzadeh, Seyed Mohammad and Sherkati, Shahram\",\"doi\":\"10.1016/j.petrol.2012.06.028\",\"journal\":\"Journal of Petroleum Science and Engineering\",\"bibtex\":\"@article{\\n title = {Application of Bayesian in determining productive zones by well log data in oil wells},\\n type = {article},\\n year = {2012},\\n keywords = {Bayesian Network,net pay,petrophysics,productive zone,well test},\\n pages = {47-54},\\n volume = {94-95},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0920410512001702},\\n month = {9},\\n id = {5e0764ec-d115-302e-b597-ef9875329209},\\n created = {2015-04-12T18:30:34.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Exploration specialists conventionally utilize a cut-off-based method to find productive zones inside the oil wells. Using conventional method, pay zones are separated crisply from non-pay zones by applying cut-off values on some petrophysical features. In this paper, a Bayesian technique is developed to find productive zones (net pays), and Bayesian Network is used to select the most appropriate input features for this newly developed method. So, two Bayesian methods were developed: the first one with conventional pay determination inputs (shale percent, porosity and water saturation), the other with two inputs, selected by Bayesian Network (porosity and water saturation). Two developed Bayesian methods are applied on well log dataset of two wells: one well is dedicated for training and testing Bayesian methods, the other for checking generalization ability of the proposed methods. Outputs of two presented methods were compared with the results of conventional cut-off-based method and production test results (i.e. a direct procedure to check validation of proposed methods). Results show that the most prominent advantage of developed Bayesian method is determination of net pays fuzzily with no need to identify cut-offs, in addition to higher precision of classification: nearly 30% improvement in precision of determining net pays of first well (training well), and about 50% improvement in precision of determining productive zones through the generalizing well.},\\n bibtype = {article},\\n author = {Masoudi, Pedram and Tokhmechi, Behzad and Ansari Jafari, Majid and Zamanzadeh, Seyed Mohammad and Sherkati, Shahram},\\n doi = {10.1016/j.petrol.2012.06.028},\\n journal = {Journal of Petroleum Science and Engineering}\\n}\",\"author_short\":[\"Masoudi,  P.\",\"Tokhmechi,  B.\",\"Ansari Jafari,  M.\",\"Zamanzadeh,  S., M.\",\"Sherkati,  S.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0920410512001702\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"masoudi-tokhmechi-ansarijafari-zamanzadeh-sherkati-applicationofbayesianindeterminingproductivezonesbywelllogdatainoilwells-2012\",\"role\":\"author\",\"keyword\":[\"Bayesian Network\",\"net pay\",\"petrophysics\",\"productive zone\",\"well test\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network.\",\"type\":\"article\",\"year\":\"2009\",\"keywords\":\"Animal Husbandry,Animals,Cattle,Cattle Diseases,Cattle Diseases: epidemiology,Cattle Diseases: prevention & control,Cross-Sectional Studies,Denmark,Denmark: epidemiology,Dermatitis,Dermatitis: epidemiology,Dermatitis: prevention & control,Dermatitis: veterinary,Female,Foot Diseases,Foot Diseases: epidemiology,Foot Diseases: prevention & control,Foot Diseases: veterinary,Hoof and Claw,Lactation,Markov Chains,Monte Carlo Method,Risk Factors,Stochastic Processes\",\"pages\":\"89-98\",\"volume\":\"92\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0167587709002323\",\"month\":\"11\",\"day\":\"1\",\"id\":\"79bf4cc8-bb0a-3cb3-bea9-905de32abe19\",\"created\":\"2015-04-12T18:44:12.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Cross sectional data on the prevalence of claw and (inter) digital skin diseases on 4854 Holstein Friesian cows in 50 Danish dairy herds was used in a Bayesian network to create herd specific probability distributions for the presence of lameness causing diseases. Parity and lactation stage are identified as risk factors on cow level, for the prevalence of the three lameness causing diseases digital dermatitits, other infectious diseases and claw horn diseases. Four herd level risk factors have been identified; herd size, the use of footbaths, a grazing strategy and total mixed ration. Besides, the data has been used to estimate the random effect of herd on disease prevalence and to find conditional probabilities of cows being lame, given the presence of the three diseases. By considering the 50 herds representative for the Danish population, the estimates for risk factors, conditional probabilities and random herd effects are used to formulate cow-level probability distributions of disease presence in a specific Danish dairy herd. By step-wise inclusion of information on cow- and herd-level risk factors, lameness prevalence and clinical diagnosis of diseases on cows in the herd, the Bayesian network systematically adjusts the probability distributions for disease presence in the specific herd. Information on population-, herd- and cow-level is combined and the uncertainty in inference on disease probability is quantified.\",\"bibtype\":\"article\",\"author\":\"Ettema, Jehan Frans and Østergaard, Søren and Kristensen, Anders Ringgaard\",\"doi\":\"10.1016/j.prevetmed.2009.08.014\",\"journal\":\"Preventive veterinary medicine\",\"number\":\"1-2\",\"bibtex\":\"@article{\\n title = {Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network.},\\n type = {article},\\n year = {2009},\\n keywords = {Animal Husbandry,Animals,Cattle,Cattle Diseases,Cattle Diseases: epidemiology,Cattle Diseases: prevention & control,Cross-Sectional Studies,Denmark,Denmark: epidemiology,Dermatitis,Dermatitis: epidemiology,Dermatitis: prevention & control,Dermatitis: veterinary,Female,Foot Diseases,Foot Diseases: epidemiology,Foot Diseases: prevention & control,Foot Diseases: veterinary,Hoof and Claw,Lactation,Markov Chains,Monte Carlo Method,Risk Factors,Stochastic Processes},\\n pages = {89-98},\\n volume = {92},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587709002323},\\n month = {11},\\n day = {1},\\n id = {79bf4cc8-bb0a-3cb3-bea9-905de32abe19},\\n created = {2015-04-12T18:44:12.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Cross sectional data on the prevalence of claw and (inter) digital skin diseases on 4854 Holstein Friesian cows in 50 Danish dairy herds was used in a Bayesian network to create herd specific probability distributions for the presence of lameness causing diseases. Parity and lactation stage are identified as risk factors on cow level, for the prevalence of the three lameness causing diseases digital dermatitits, other infectious diseases and claw horn diseases. Four herd level risk factors have been identified; herd size, the use of footbaths, a grazing strategy and total mixed ration. Besides, the data has been used to estimate the random effect of herd on disease prevalence and to find conditional probabilities of cows being lame, given the presence of the three diseases. By considering the 50 herds representative for the Danish population, the estimates for risk factors, conditional probabilities and random herd effects are used to formulate cow-level probability distributions of disease presence in a specific Danish dairy herd. By step-wise inclusion of information on cow- and herd-level risk factors, lameness prevalence and clinical diagnosis of diseases on cows in the herd, the Bayesian network systematically adjusts the probability distributions for disease presence in the specific herd. Information on population-, herd- and cow-level is combined and the uncertainty in inference on disease probability is quantified.},\\n bibtype = {article},\\n author = {Ettema, Jehan Frans and Østergaard, Søren and Kristensen, Anders Ringgaard},\\n doi = {10.1016/j.prevetmed.2009.08.014},\\n journal = {Preventive veterinary medicine},\\n number = {1-2}\\n}\",\"author_short\":[\"Ettema,  J., F.\",\"Østergaard,  S.\",\"Kristensen,  A., R.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0167587709002323\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"ettema-stergaard-kristensen-estimationofprobabilityforthepresenceofclawanddigitalskindiseasesbycombiningcowandherdlevelinformationusingabayesiannetwork-2009\",\"role\":\"author\",\"keyword\":[\"Animal Husbandry\",\"Animals\",\"Cattle\",\"Cattle Diseases\",\"Cattle Diseases: epidemiology\",\"Cattle Diseases: prevention & control\",\"Cross-Sectional Studies\",\"Denmark\",\"Denmark: epidemiology\",\"Dermatitis\",\"Dermatitis: epidemiology\",\"Dermatitis: prevention & control\",\"Dermatitis: veterinary\",\"Female\",\"Foot Diseases\",\"Foot Diseases: epidemiology\",\"Foot Diseases: prevention & control\",\"Foot Diseases: veterinary\",\"Hoof and Claw\",\"Lactation\",\"Markov Chains\",\"Monte Carlo Method\",\"Risk Factors\",\"Stochastic Processes\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species\",\"type\":\"article\",\"year\":\"2015\",\"keywords\":\"Bayesian networks,Fisheries management,Kernel density estimation,Pelagic fish,Recruitment forecasting,Supervised classification\",\"pages\":\"35-42\",\"volume\":\"25\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1574954114001563\",\"month\":\"1\",\"id\":\"88652dfa-d3a0-3a29-a600-b67b9c00fa5c\",\"created\":\"2015-04-12T18:44:12.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.\",\"bibtype\":\"article\",\"author\":\"Fernandes, Jose A. and Irigoien, Xabier and Lozano, Jose A. and Inza, Iñaki and Goikoetxea, Nerea and Pérez, Aritz\",\"doi\":\"10.1016/j.ecoinf.2014.11.004\",\"journal\":\"Ecological Informatics\",\"bibtex\":\"@article{\\n title = {Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species},\\n type = {article},\\n year = {2015},\\n keywords = {Bayesian networks,Fisheries management,Kernel density estimation,Pelagic fish,Recruitment forecasting,Supervised classification},\\n pages = {35-42},\\n volume = {25},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1574954114001563},\\n month = {1},\\n id = {88652dfa-d3a0-3a29-a600-b67b9c00fa5c},\\n created = {2015-04-12T18:44:12.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.},\\n bibtype = {article},\\n author = {Fernandes, Jose A. and Irigoien, Xabier and Lozano, Jose A. and Inza, Iñaki and Goikoetxea, Nerea and Pérez, Aritz},\\n doi = {10.1016/j.ecoinf.2014.11.004},\\n journal = {Ecological Informatics}\\n}\",\"author_short\":[\"Fernandes,  J., A.\",\"Irigoien,  X.\",\"Lozano,  J., A.\",\"Inza,  I.\",\"Goikoetxea,  N.\",\"Pérez,  A.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1574954114001563\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"fernandes-irigoien-lozano-inza-goikoetxea-prez-evaluatingmachinelearningtechniquesforrecruitmentforecastingofsevennortheastatlanticfishspecies-2015\",\"role\":\"author\",\"keyword\":[\"Bayesian networks\",\"Fisheries management\",\"Kernel density estimation\",\"Pelagic fish\",\"Recruitment forecasting\",\"Supervised classification\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).\",\"type\":\"article\",\"year\":\"2012\",\"keywords\":\"Bayes Theorem,Health Status,Humans,Markov Chains,Parkinson Disease,Quality of Life,Questionnaires\",\"pages\":\"1175-84\",\"volume\":\"45\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1532046412001074\",\"month\":\"12\",\"id\":\"7c918dcd-9adb-3846-a93a-08f76992fe8d\",\"created\":\"2015-04-12T18:44:13.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.\",\"bibtype\":\"article\",\"author\":\"Borchani, Hanen and Bielza, Concha and Martı Nez-Martı N, Pablo and Larrañaga, Pedro\",\"doi\":\"10.1016/j.jbi.2012.07.010\",\"journal\":\"Journal of biomedical informatics\",\"number\":\"6\",\"bibtex\":\"@article{\\n title = {Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).},\\n type = {article},\\n year = {2012},\\n keywords = {Bayes Theorem,Health Status,Humans,Markov Chains,Parkinson Disease,Quality of Life,Questionnaires},\\n pages = {1175-84},\\n volume = {45},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046412001074},\\n month = {12},\\n id = {7c918dcd-9adb-3846-a93a-08f76992fe8d},\\n created = {2015-04-12T18:44:13.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.},\\n bibtype = {article},\\n author = {Borchani, Hanen and Bielza, Concha and Martı Nez-Martı N, Pablo and Larrañaga, Pedro},\\n doi = {10.1016/j.jbi.2012.07.010},\\n journal = {Journal of biomedical informatics},\\n number = {6}\\n}\",\"author_short\":[\"Borchani,  H.\",\"Bielza,  C.\",\"Martı Nez-Martı N,  P.\",\"Larrañaga,  P.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1532046412001074\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"borchani-bielza-martnezmartn-larraaga-markovblanketbasedapproachforlearningmultidimensionalbayesiannetworkclassifiersanapplicationtopredicttheeuropeanqualityoflife5dimensionseq5dfromthe39itemparkinsonsdiseasequestionnairepdq39-2012\",\"role\":\"author\",\"keyword\":[\"Bayes Theorem\",\"Health Status\",\"Humans\",\"Markov Chains\",\"Parkinson Disease\",\"Quality of Life\",\"Questionnaires\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A Bayesian network model for predicting pregnancy after in vitro fertilization.\",\"type\":\"article\",\"year\":\"2013\",\"keywords\":\"Adult,Algorithms,Area Under Curve,Bayes Theorem,Computer Simulation,Embryo Transfer,Embryo Transfer: statistics & numerical data,Embryo, Mammalian,Female,Fertilization in Vitro,Fertilization in Vitro: statistics & numerical dat,Humans,Pregnancy,Pregnancy: statistics & numerical data\",\"pages\":\"1783-92\",\"volume\":\"43\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0010482513002187\",\"month\":\"11\",\"id\":\"ea7467fd-0819-3352-ac67-6489e0f12820\",\"created\":\"2015-04-12T18:44:13.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"We present a Bayesian network model for predicting the outcome of in vitro fertilization (IVF). The problem is characterized by a particular missingness process; we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. We present results with generated data and the analysis of a real data set. Moreover, we assess by means of a simulation study the effectiveness of the model in supporting the selection of the embryos to be transferred.\",\"bibtype\":\"article\",\"author\":\"Corani, G and Magli, C and Giusti, A and Gianaroli, L and Gambardella, L M\",\"doi\":\"10.1016/j.compbiomed.2013.07.035\",\"journal\":\"Computers in biology and medicine\",\"number\":\"11\",\"bibtex\":\"@article{\\n title = {A Bayesian network model for predicting pregnancy after in vitro fertilization.},\\n type = {article},\\n year = {2013},\\n keywords = {Adult,Algorithms,Area Under Curve,Bayes Theorem,Computer Simulation,Embryo Transfer,Embryo Transfer: statistics & numerical data,Embryo, Mammalian,Female,Fertilization in Vitro,Fertilization in Vitro: statistics & numerical dat,Humans,Pregnancy,Pregnancy: statistics & numerical data},\\n pages = {1783-92},\\n volume = {43},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482513002187},\\n month = {11},\\n id = {ea7467fd-0819-3352-ac67-6489e0f12820},\\n created = {2015-04-12T18:44:13.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {We present a Bayesian network model for predicting the outcome of in vitro fertilization (IVF). The problem is characterized by a particular missingness process; we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. We present results with generated data and the analysis of a real data set. Moreover, we assess by means of a simulation study the effectiveness of the model in supporting the selection of the embryos to be transferred.},\\n bibtype = {article},\\n author = {Corani, G and Magli, C and Giusti, A and Gianaroli, L and Gambardella, L M},\\n doi = {10.1016/j.compbiomed.2013.07.035},\\n journal = {Computers in biology and medicine},\\n number = {11}\\n}\",\"author_short\":[\"Corani,  G.\",\"Magli,  C.\",\"Giusti,  A.\",\"Gianaroli,  L.\",\"Gambardella,  L., M.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0010482513002187\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"corani-magli-giusti-gianaroli-gambardella-abayesiannetworkmodelforpredictingpregnancyafterinvitrofertilization-2013\",\"role\":\"author\",\"keyword\":[\"Adult\",\"Algorithms\",\"Area Under Curve\",\"Bayes Theorem\",\"Computer Simulation\",\"Embryo Transfer\",\"Embryo Transfer: statistics & numerical data\",\"Embryo\",\"Mammalian\",\"Female\",\"Fertilization in Vitro\",\"Fertilization in Vitro: statistics & numerical dat\",\"Humans\",\"Pregnancy\",\"Pregnancy: statistics & numerical data\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Diagnosis of breast cancer using Bayesian networks: a case study.\",\"type\":\"article\",\"year\":\"2007\",\"keywords\":\"Algorithms,Bayes Theorem,Biopsy, Fine-Needle,Breast Neoplasms,Breast Neoplasms: diagnosis,Cytodiagnosis,Cytodiagnosis: statistics & numerical data,Databases, Factual,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: statistics & numeric,Female,Humans,Observer Variation\",\"pages\":\"1553-64\",\"volume\":\"37\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0010482507000340\",\"month\":\"11\",\"id\":\"c166c75a-987f-386e-9dbf-98e6a0dc37eb\",\"created\":\"2015-04-12T18:44:13.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"We evaluate the effectiveness of seven Bayesian network classifiers as potential tools for the diagnosis of breast cancer using two real-world databases containing fine-needle aspiration of the breast lesion cases collected by a single observer and multiple observers, respectively. The results show a certain ingredient of subjectivity implicitly contained in these data: we get an average accuracy of 93.04% for the former and 83.31% for the latter. These findings suggest that observers see different things when looking at the samples in the microscope; a situation that significantly diminishes the performance of these classifiers in diagnosing such a disease.\",\"bibtype\":\"article\",\"author\":\"Cruz-Ramírez, Nicandro and Acosta-Mesa, Héctor Gabriel and Carrillo-Calvet, Humberto and Nava-Fernández, Luis Alonso and Barrientos-Martínez, Rocío Erandi\",\"doi\":\"10.1016/j.compbiomed.2007.02.003\",\"journal\":\"Computers in biology and medicine\",\"number\":\"11\",\"bibtex\":\"@article{\\n title = {Diagnosis of breast cancer using Bayesian networks: a case study.},\\n type = {article},\\n year = {2007},\\n keywords = {Algorithms,Bayes Theorem,Biopsy, Fine-Needle,Breast Neoplasms,Breast Neoplasms: diagnosis,Cytodiagnosis,Cytodiagnosis: statistics & numerical data,Databases, Factual,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: statistics & numeric,Female,Humans,Observer Variation},\\n pages = {1553-64},\\n volume = {37},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482507000340},\\n month = {11},\\n id = {c166c75a-987f-386e-9dbf-98e6a0dc37eb},\\n created = {2015-04-12T18:44:13.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {We evaluate the effectiveness of seven Bayesian network classifiers as potential tools for the diagnosis of breast cancer using two real-world databases containing fine-needle aspiration of the breast lesion cases collected by a single observer and multiple observers, respectively. The results show a certain ingredient of subjectivity implicitly contained in these data: we get an average accuracy of 93.04% for the former and 83.31% for the latter. These findings suggest that observers see different things when looking at the samples in the microscope; a situation that significantly diminishes the performance of these classifiers in diagnosing such a disease.},\\n bibtype = {article},\\n author = {Cruz-Ramírez, Nicandro and Acosta-Mesa, Héctor Gabriel and Carrillo-Calvet, Humberto and Nava-Fernández, Luis Alonso and Barrientos-Martínez, Rocío Erandi},\\n doi = {10.1016/j.compbiomed.2007.02.003},\\n journal = {Computers in biology and medicine},\\n number = {11}\\n}\",\"author_short\":[\"Cruz-Ramírez,  N.\",\"Acosta-Mesa,  H., G.\",\"Carrillo-Calvet,  H.\",\"Nava-Fernández,  L., A.\",\"Barrientos-Martínez,  R., E.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0010482507000340\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"cruzramrez-acostamesa-carrillocalvet-navafernndez-barrientosmartnez-diagnosisofbreastcancerusingbayesiannetworksacasestudy-2007\",\"role\":\"author\",\"keyword\":[\"Algorithms\",\"Bayes Theorem\",\"Biopsy\",\"Fine-Needle\",\"Breast Neoplasms\",\"Breast Neoplasms: diagnosis\",\"Cytodiagnosis\",\"Cytodiagnosis: statistics & numerical data\",\"Databases\",\"Factual\",\"Diagnosis\",\"Computer-Assisted\",\"Diagnosis\",\"Computer-Assisted: statistics & numeric\",\"Female\",\"Humans\",\"Observer Variation\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A data mining approach to predictive vegetation mapping using probabilistic graphical models\",\"type\":\"article\",\"year\":\"2011\",\"keywords\":\"Bayesian network,Data mining,Expectation-maximization,Graphical model,Predictive vegetation mapping\",\"pages\":\"111-124\",\"volume\":\"6\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1574954111000045\",\"month\":\"3\",\"id\":\"4b6617c5-cc10-33bd-a91f-6af94ae4a28f\",\"created\":\"2015-04-12T18:44:13.000Z\",\"accessed\":\"2015-02-16\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"This paper develops a novel method to model and predict the spatial distribution of vegetation types in Swaziland using physiographic and bioclimatic variables. The method uses a data mining approach implemented within a probabilistic graphical model to match two observed hierarchical levels of vegetation. The classification uses Bayesian networks (BN) and the parameterization is based on the expectation-maximization (EM) algorithm. The model is tested on a random sample of mapped vegetation types in Swaziland and allowed for the identification of the key environmental variables that are most important for capturing the vegetation spatial distribution. We show that while elevation and geology are the most important variables explaining the spatial distribution patterns of vegetation for both models, the influence of the climatic and other variables on the vegetation at the two levels differ. The overall distribution of the predicted vegetation classes was very similar to their distribution on the observed vegetation map. Overall the error rate was found to be 9.35% for a model of 16 vegetation classes and 4.9% for the one with 5 classes, indicating the excellent classification accuracy of the approach despite the complex landscape of the study area. Possible sources of error and some limitations are discussed and conclusions are drawn including suggestions for further investigation.\",\"bibtype\":\"article\",\"author\":\"Dlamini, Wisdom M.\",\"doi\":\"10.1016/j.ecoinf.2010.12.005\",\"journal\":\"Ecological Informatics\",\"number\":\"2\",\"bibtex\":\"@article{\\n title = {A data mining approach to predictive vegetation mapping using probabilistic graphical models},\\n type = {article},\\n year = {2011},\\n keywords = {Bayesian network,Data mining,Expectation-maximization,Graphical model,Predictive vegetation mapping},\\n pages = {111-124},\\n volume = {6},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1574954111000045},\\n month = {3},\\n id = {4b6617c5-cc10-33bd-a91f-6af94ae4a28f},\\n created = {2015-04-12T18:44:13.000Z},\\n accessed = {2015-02-16},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {This paper develops a novel method to model and predict the spatial distribution of vegetation types in Swaziland using physiographic and bioclimatic variables. The method uses a data mining approach implemented within a probabilistic graphical model to match two observed hierarchical levels of vegetation. The classification uses Bayesian networks (BN) and the parameterization is based on the expectation-maximization (EM) algorithm. The model is tested on a random sample of mapped vegetation types in Swaziland and allowed for the identification of the key environmental variables that are most important for capturing the vegetation spatial distribution. We show that while elevation and geology are the most important variables explaining the spatial distribution patterns of vegetation for both models, the influence of the climatic and other variables on the vegetation at the two levels differ. The overall distribution of the predicted vegetation classes was very similar to their distribution on the observed vegetation map. Overall the error rate was found to be 9.35% for a model of 16 vegetation classes and 4.9% for the one with 5 classes, indicating the excellent classification accuracy of the approach despite the complex landscape of the study area. Possible sources of error and some limitations are discussed and conclusions are drawn including suggestions for further investigation.},\\n bibtype = {article},\\n author = {Dlamini, Wisdom M.},\\n doi = {10.1016/j.ecoinf.2010.12.005},\\n journal = {Ecological Informatics},\\n number = {2}\\n}\",\"author_short\":[\"Dlamini,  W., M.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1574954111000045\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"dlamini-adataminingapproachtopredictivevegetationmappingusingprobabilisticgraphicalmodels-2011\",\"role\":\"author\",\"keyword\":[\"Bayesian network\",\"Data mining\",\"Expectation-maximization\",\"Graphical model\",\"Predictive vegetation mapping\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Bayesian network,Development,Habitat,Piping plover,Sea-level rise,Shorebird\",\"pages\":\"38-50\",\"volume\":\"276\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0304380014000398\",\"month\":\"3\",\"id\":\"086f8466-6471-3150-a892-146a13a8d70a\",\"created\":\"2015-04-12T18:51:29.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modeling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model's dataset. We found that model predictions were more successful when the ranges of physical conditions included in model development were varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modeling impacts of sea-level rise or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.\",\"bibtype\":\"article\",\"author\":\"Gieder, Katherina D. and Karpanty, Sarah M. and Fraser, James D. and Catlin, Daniel H. and Gutierrez, Benjamin T. and Plant, Nathaniel G. and Turecek, Aaron M. and Robert Thieler, E.\",\"doi\":\"10.1016/j.ecolmodel.2014.01.005\",\"journal\":\"Ecological Modelling\",\"bibtex\":\"@article{\\n title = {A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features},\\n type = {article},\\n year = {2014},\\n keywords = {Bayesian network,Development,Habitat,Piping plover,Sea-level rise,Shorebird},\\n pages = {38-50},\\n volume = {276},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380014000398},\\n month = {3},\\n id = {086f8466-6471-3150-a892-146a13a8d70a},\\n created = {2015-04-12T18:51:29.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modeling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model's dataset. We found that model predictions were more successful when the ranges of physical conditions included in model development were varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modeling impacts of sea-level rise or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.},\\n bibtype = {article},\\n author = {Gieder, Katherina D. and Karpanty, Sarah M. and Fraser, James D. and Catlin, Daniel H. and Gutierrez, Benjamin T. and Plant, Nathaniel G. and Turecek, Aaron M. and Robert Thieler, E.},\\n doi = {10.1016/j.ecolmodel.2014.01.005},\\n journal = {Ecological Modelling}\\n}\",\"author_short\":[\"Gieder,  K., D.\",\"Karpanty,  S., M.\",\"Fraser,  J., D.\",\"Catlin,  D., H.\",\"Gutierrez,  B., T.\",\"Plant,  N., G.\",\"Turecek,  A., M.\",\"Robert Thieler,  E.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0304380014000398\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014\",\"role\":\"author\",\"keyword\":[\"Bayesian network\",\"Development\",\"Habitat\",\"Piping plover\",\"Sea-level rise\",\"Shorebird\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Learning and modeling biosignatures from tissue images.\",\"type\":\"article\",\"year\":\"2007\",\"keywords\":\"Algorithms,Animals,Artificial Intelligence,Bayes Theorem,Computer Simulation,Diagnosis, Computer-Assisted,Humans,Image Interpretation, Computer-Assisted,Infection,Infection: classification,Infection: diagnosis,Lung,Lung Diseases,Lung Diseases: diagnosis,Lung: anatomy & histology,Mice\",\"pages\":\"1539-52\",\"volume\":\"37\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0010482507000339\",\"month\":\"11\",\"id\":\"57bfce04-027f-341a-a291-194cee674ec5\",\"created\":\"2015-04-12T18:51:29.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Ideally biosignatures can be detected at the early infection phase and used both for developing diagnostic patterns and for prognostic triage. Such biosignatures are important for vaccine validation and to provide risk stratification to a population such as for the identification of individuals who are exposed to biological or chemical agents and who are at high risk for developing an infection. The research goal is to detect broad based biosignature models and is initially focused on developing effective computer-augmented pathology tied to animal models developed at the University of New Mexico (UNM). Using lung tissue from infected and nai ve mice, feature extraction from images of the tissue under a specialized microscope, and Bayesian networks to analyze the data sets of features, we were able to differentiate normal from diseased samples and viral from bacterial samples in mid to late stages of infection. This effort has shown the potential effectiveness of computer-augmented pathology in this application. The extended research intends to couple analysis of serum, microarray analysis of organs, proteomic data and the pathology. The rational for the current invasive procedure on animal models is to facilitate the development of data analysis and machine learning techniques that can eventually be generalized to the task of discovering non-invasive and early stage biosignatures for human models.\",\"bibtype\":\"article\",\"author\":\"Gilfeather, Frank and Hamine, Vikas and Helman, Paul and Hutt, Julie and Loring, Terry and Lyons, C Rick and Veroff, Robert\",\"doi\":\"10.1016/j.compbiomed.2007.02.005\",\"journal\":\"Computers in biology and medicine\",\"number\":\"11\",\"bibtex\":\"@article{\\n title = {Learning and modeling biosignatures from tissue images.},\\n type = {article},\\n year = {2007},\\n keywords = {Algorithms,Animals,Artificial Intelligence,Bayes Theorem,Computer Simulation,Diagnosis, Computer-Assisted,Humans,Image Interpretation, Computer-Assisted,Infection,Infection: classification,Infection: diagnosis,Lung,Lung Diseases,Lung Diseases: diagnosis,Lung: anatomy & histology,Mice},\\n pages = {1539-52},\\n volume = {37},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482507000339},\\n month = {11},\\n id = {57bfce04-027f-341a-a291-194cee674ec5},\\n created = {2015-04-12T18:51:29.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Ideally biosignatures can be detected at the early infection phase and used both for developing diagnostic patterns and for prognostic triage. Such biosignatures are important for vaccine validation and to provide risk stratification to a population such as for the identification of individuals who are exposed to biological or chemical agents and who are at high risk for developing an infection. The research goal is to detect broad based biosignature models and is initially focused on developing effective computer-augmented pathology tied to animal models developed at the University of New Mexico (UNM). Using lung tissue from infected and nai ve mice, feature extraction from images of the tissue under a specialized microscope, and Bayesian networks to analyze the data sets of features, we were able to differentiate normal from diseased samples and viral from bacterial samples in mid to late stages of infection. This effort has shown the potential effectiveness of computer-augmented pathology in this application. The extended research intends to couple analysis of serum, microarray analysis of organs, proteomic data and the pathology. The rational for the current invasive procedure on animal models is to facilitate the development of data analysis and machine learning techniques that can eventually be generalized to the task of discovering non-invasive and early stage biosignatures for human models.},\\n bibtype = {article},\\n author = {Gilfeather, Frank and Hamine, Vikas and Helman, Paul and Hutt, Julie and Loring, Terry and Lyons, C Rick and Veroff, Robert},\\n doi = {10.1016/j.compbiomed.2007.02.005},\\n journal = {Computers in biology and medicine},\\n number = {11}\\n}\",\"author_short\":[\"Gilfeather,  F.\",\"Hamine,  V.\",\"Helman,  P.\",\"Hutt,  J.\",\"Loring,  T.\",\"Lyons,  C., R.\",\"Veroff,  R.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0010482507000339\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"gilfeather-hamine-helman-hutt-loring-lyons-veroff-learningandmodelingbiosignaturesfromtissueimages-2007\",\"role\":\"author\",\"keyword\":[\"Algorithms\",\"Animals\",\"Artificial Intelligence\",\"Bayes Theorem\",\"Computer Simulation\",\"Diagnosis\",\"Computer-Assisted\",\"Humans\",\"Image Interpretation\",\"Computer-Assisted\",\"Infection\",\"Infection: classification\",\"Infection: diagnosis\",\"Lung\",\"Lung Diseases\",\"Lung Diseases: diagnosis\",\"Lung: anatomy & histology\",\"Mice\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A meta-assessment for elasmobranchs based on dietary data and Bayesian networks\",\"type\":\"article\",\"year\":\"2002\",\"keywords\":\"Bayesian networks,Elasmobranchs,Fisheries management,Food webs,Threatened species\",\"pages\":\"197-211\",\"volume\":\"1\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1470160X02000055\",\"month\":\"3\",\"id\":\"043feed1-8366-3a45-a06f-512aa682a2de\",\"created\":\"2015-04-12T18:51:29.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"We developed a new approach, meta-assessment, as a tool for identifying declining (and potentially threatened) fish stocks in situations where a lack of data precludes traditional stock assessments. Meta-assessments are models enhanced by the incorporation of other stock assessment results. We used this idea to estimate historic biomass trends for demersal elasmobranchs of the Irish Sea. Bayesian networks, constructed from published dietary data and resembling food webs, allowed us to incorporate into our estimates the results from virtual population analysis (VPA) for Irish Sea cod, sole, plaice and whiting. To assess accuracy, we used cross-validation, estimating historic biomass trends in each individual VPA species from trends in the other three plus trends in fishing effort. We compared predicted annual trends to those derived from VPA and found 66% accuracy. We also compared biomass trends estimated from annual trawl surveys to corresponding network predictions, recovering survey trends correctly 61% of the time for elasmobranchs, 78% of the time for gurnards (Triglidae) and 89% for bib and pout (Trisopterus spp.). Results suggest that of the 11 elasmobranchs examined, the angel shark (Squatina squatina) increased in biomass least frequently from 1987 to 1997, a view consistent with survey results. Our approach also suggested a marked decline in common skate (Dipturus batis) over the period 1965–1978, during which time the skate was extirpated from the Irish Sea. We conclude that meta-assessment can serve as an exploratory method for identifying potentially threatened stocks, where even landings data are unavailable.\",\"bibtype\":\"article\",\"author\":\"Hammond, T.R and Ellis, J.R\",\"doi\":\"10.1016/S1470-160X(02)00005-5\",\"journal\":\"Ecological Indicators\",\"number\":\"3\",\"bibtex\":\"@article{\\n title = {A meta-assessment for elasmobranchs based on dietary data and Bayesian networks},\\n type = {article},\\n year = {2002},\\n keywords = {Bayesian networks,Elasmobranchs,Fisheries management,Food webs,Threatened species},\\n pages = {197-211},\\n volume = {1},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1470160X02000055},\\n month = {3},\\n id = {043feed1-8366-3a45-a06f-512aa682a2de},\\n created = {2015-04-12T18:51:29.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {We developed a new approach, meta-assessment, as a tool for identifying declining (and potentially threatened) fish stocks in situations where a lack of data precludes traditional stock assessments. Meta-assessments are models enhanced by the incorporation of other stock assessment results. We used this idea to estimate historic biomass trends for demersal elasmobranchs of the Irish Sea. Bayesian networks, constructed from published dietary data and resembling food webs, allowed us to incorporate into our estimates the results from virtual population analysis (VPA) for Irish Sea cod, sole, plaice and whiting. To assess accuracy, we used cross-validation, estimating historic biomass trends in each individual VPA species from trends in the other three plus trends in fishing effort. We compared predicted annual trends to those derived from VPA and found 66% accuracy. We also compared biomass trends estimated from annual trawl surveys to corresponding network predictions, recovering survey trends correctly 61% of the time for elasmobranchs, 78% of the time for gurnards (Triglidae) and 89% for bib and pout (Trisopterus spp.). Results suggest that of the 11 elasmobranchs examined, the angel shark (Squatina squatina) increased in biomass least frequently from 1987 to 1997, a view consistent with survey results. Our approach also suggested a marked decline in common skate (Dipturus batis) over the period 1965–1978, during which time the skate was extirpated from the Irish Sea. We conclude that meta-assessment can serve as an exploratory method for identifying potentially threatened stocks, where even landings data are unavailable.},\\n bibtype = {article},\\n author = {Hammond, T.R and Ellis, J.R},\\n doi = {10.1016/S1470-160X(02)00005-5},\\n journal = {Ecological Indicators},\\n number = {3}\\n}\",\"author_short\":[\"Hammond,  T.\",\"Ellis,  J.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1470160X02000055\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"hammond-ellis-ametaassessmentforelasmobranchsbasedondietarydataandbayesiannetworks-2002\",\"role\":\"author\",\"keyword\":[\"Bayesian networks\",\"Elasmobranchs\",\"Fisheries management\",\"Food webs\",\"Threatened species\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Quantifying the determinants of outbreak detection performance through simulation and machine learning.\",\"type\":\"article\",\"year\":\"2015\",\"keywords\":\"Bayesian networks,Disease outbreak detection,Outbreak simulation,Predicting performance,Public health informatics,Surveillance\",\"pages\":\"180-7\",\"volume\":\"53\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1532046414002299\",\"month\":\"2\",\"id\":\"d7500789-9ac9-3544-924e-f336004042ac\",\"created\":\"2015-04-12T18:51:29.000Z\",\"accessed\":\"2015-03-17\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.\\n\\nMATERIALS AND METHODS: We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation.\\n\\nRESULTS: The model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns.\\n\\nCONCLUSION: We developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems.\",\"bibtype\":\"article\",\"author\":\"Jafarpour, Nastaran and Izadi, Masoumeh and Precup, Doina and Buckeridge, David L\",\"doi\":\"10.1016/j.jbi.2014.10.009\",\"journal\":\"Journal of biomedical informatics\",\"bibtex\":\"@article{\\n title = {Quantifying the determinants of outbreak detection performance through simulation and machine learning.},\\n type = {article},\\n year = {2015},\\n keywords = {Bayesian networks,Disease outbreak detection,Outbreak simulation,Predicting performance,Public health informatics,Surveillance},\\n pages = {180-7},\\n volume = {53},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046414002299},\\n month = {2},\\n id = {d7500789-9ac9-3544-924e-f336004042ac},\\n created = {2015-04-12T18:51:29.000Z},\\n accessed = {2015-03-17},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.\\n\\nMATERIALS AND METHODS: We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation.\\n\\nRESULTS: The model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns.\\n\\nCONCLUSION: We developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems.},\\n bibtype = {article},\\n author = {Jafarpour, Nastaran and Izadi, Masoumeh and Precup, Doina and Buckeridge, David L},\\n doi = {10.1016/j.jbi.2014.10.009},\\n journal = {Journal of biomedical informatics}\\n}\",\"author_short\":[\"Jafarpour,  N.\",\"Izadi,  M.\",\"Precup,  D.\",\"Buckeridge,  D., L.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1532046414002299\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"jafarpour-izadi-precup-buckeridge-quantifyingthedeterminantsofoutbreakdetectionperformancethroughsimulationandmachinelearning-2015\",\"role\":\"author\",\"keyword\":[\"Bayesian networks\",\"Disease outbreak detection\",\"Outbreak simulation\",\"Predicting performance\",\"Public health informatics\",\"Surveillance\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Machine learning applications in cancer prognosis and prediction\",\"type\":\"article\",\"year\":\"2015\",\"keywords\":\"ANN,AUC,Area Under Curve,Artificial Neural Network,BCRSVM,BN,Bayesian Network,Breast Cancer Support Vector Machine,CFS,Cancer recurrence,Cancer survival,Cancer susceptibility,Correlation based Feature Selection,DT,Decision Tree,ES,Early Stopping algorithm,GEO,Gene Expression Omnibus,HTT,High-throughput Technologies,LCS,Learning Classifying Systems,ML,Machine Learning,Machine learning,NCI caArray,NSCLC,National Cancer Institute Array Data Management Sy,Non-small Cell Lung Cancer,OSCC,Oral Squamous Cell Carcinoma,PPI,Predictive models,Protein–Protein Interaction,ROC,Receiver Operating Characteristic,SEER,SSL,SVM,Semi-supervised Learning,Support Vector Machine,Surveillance, Epidemiology and End results Databas,TCGA,The Cancer Genome Atlas Research Network\",\"pages\":\"8-17\",\"volume\":\"13\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S2001037014000464\",\"month\":\"12\",\"id\":\"6549cc53-5dde-30fc-94cf-a29fdd5890b5\",\"created\":\"2015-04-12T18:59:39.000Z\",\"accessed\":\"2015-01-01\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.\",\"bibtype\":\"article\",\"author\":\"Kourou, Konstantina and Exarchos, Themis P. and Exarchos, Konstantinos P. and Karamouzis, Michalis V. and Fotiadis, Dimitrios I.\",\"doi\":\"10.1016/j.csbj.2014.11.005\",\"journal\":\"Computational and Structural Biotechnology Journal\",\"bibtex\":\"@article{\\n title = {Machine learning applications in cancer prognosis and prediction},\\n type = {article},\\n year = {2015},\\n keywords = {ANN,AUC,Area Under Curve,Artificial Neural Network,BCRSVM,BN,Bayesian Network,Breast Cancer Support Vector Machine,CFS,Cancer recurrence,Cancer survival,Cancer susceptibility,Correlation based Feature Selection,DT,Decision Tree,ES,Early Stopping algorithm,GEO,Gene Expression Omnibus,HTT,High-throughput Technologies,LCS,Learning Classifying Systems,ML,Machine Learning,Machine learning,NCI caArray,NSCLC,National Cancer Institute Array Data Management Sy,Non-small Cell Lung Cancer,OSCC,Oral Squamous Cell Carcinoma,PPI,Predictive models,Protein–Protein Interaction,ROC,Receiver Operating Characteristic,SEER,SSL,SVM,Semi-supervised Learning,Support Vector Machine,Surveillance, Epidemiology and End results Databas,TCGA,The Cancer Genome Atlas Research Network},\\n pages = {8-17},\\n volume = {13},\\n websites = {http://www.sciencedirect.com/science/article/pii/S2001037014000464},\\n month = {12},\\n id = {6549cc53-5dde-30fc-94cf-a29fdd5890b5},\\n created = {2015-04-12T18:59:39.000Z},\\n accessed = {2015-01-01},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.},\\n bibtype = {article},\\n author = {Kourou, Konstantina and Exarchos, Themis P. and Exarchos, Konstantinos P. and Karamouzis, Michalis V. and Fotiadis, Dimitrios I.},\\n doi = {10.1016/j.csbj.2014.11.005},\\n journal = {Computational and Structural Biotechnology Journal}\\n}\",\"author_short\":[\"Kourou,  K.\",\"Exarchos,  T., P.\",\"Exarchos,  K., P.\",\"Karamouzis,  M., V.\",\"Fotiadis,  D., I.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S2001037014000464\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"kourou-exarchos-exarchos-karamouzis-fotiadis-machinelearningapplicationsincancerprognosisandprediction-2015\",\"role\":\"author\",\"keyword\":[\"ANN\",\"AUC\",\"Area Under Curve\",\"Artificial Neural Network\",\"BCRSVM\",\"BN\",\"Bayesian Network\",\"Breast Cancer Support Vector Machine\",\"CFS\",\"Cancer recurrence\",\"Cancer survival\",\"Cancer susceptibility\",\"Correlation based Feature Selection\",\"DT\",\"Decision Tree\",\"ES\",\"Early Stopping algorithm\",\"GEO\",\"Gene Expression Omnibus\",\"HTT\",\"High-throughput Technologies\",\"LCS\",\"Learning Classifying Systems\",\"ML\",\"Machine Learning\",\"Machine learning\",\"NCI caArray\",\"NSCLC\",\"National Cancer Institute Array Data Management Sy\",\"Non-small Cell Lung Cancer\",\"OSCC\",\"Oral Squamous Cell Carcinoma\",\"PPI\",\"Predictive models\",\"Protein–Protein Interaction\",\"ROC\",\"Receiver Operating Characteristic\",\"SEER\",\"SSL\",\"SVM\",\"Semi-supervised Learning\",\"Support Vector Machine\",\"Surveillance\",\"Epidemiology and End results Databas\",\"TCGA\",\"The Cancer Genome Atlas Research Network\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data\",\"type\":\"article\",\"year\":\"2011\",\"keywords\":\"Knowledge Discovery\",\"pages\":\"109-115\",\"volume\":\"100\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0167587711000341\",\"month\":\"6\",\"id\":\"d7020f3c-f6cc-3407-b484-b635b8d5047b\",\"created\":\"2015-04-12T19:14:40.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Analysing animal health data can be a complex task as the health status of individuals or groups of animals, might depend on many inter-related variables. The objective is to differentiate variables that are directly associated with health status and therefore promising targets for intervention, from variables that are indirectly associated with health status and can therefore at best only affect this indirectly through association with other variables. Bayesian network (BN) modelling is a machine learning technique for empirically identifying associations in complex and high dimensional data, so-called “structure discovery”. An introduction to structure discovery using BN modelling is presented, comprising the key assumptions required by the methodology, along with a discussion of advantages and limitations. To demonstrate the various steps required to apply BN structure discovery to animal health data, illustrative analyses of data collected during a previously published study concerned with exposure to bovine viral diarrhoea virus in beef cow-calf herds in Scotland are presented.\",\"bibtype\":\"article\",\"author\":\"Lewis, F.I. and Brülisauer, F. and Gunn, G.J.\",\"doi\":\"10.1016/j.prevetmed.2011.02.003\",\"journal\":\"Preventive Veterinary Medicine\",\"number\":\"2\",\"bibtex\":\"@article{\\n title = {Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data},\\n type = {article},\\n year = {2011},\\n keywords = {Knowledge Discovery},\\n pages = {109-115},\\n volume = {100},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587711000341},\\n month = {6},\\n id = {d7020f3c-f6cc-3407-b484-b635b8d5047b},\\n created = {2015-04-12T19:14:40.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Analysing animal health data can be a complex task as the health status of individuals or groups of animals, might depend on many inter-related variables. The objective is to differentiate variables that are directly associated with health status and therefore promising targets for intervention, from variables that are indirectly associated with health status and can therefore at best only affect this indirectly through association with other variables. Bayesian network (BN) modelling is a machine learning technique for empirically identifying associations in complex and high dimensional data, so-called “structure discovery”. An introduction to structure discovery using BN modelling is presented, comprising the key assumptions required by the methodology, along with a discussion of advantages and limitations. To demonstrate the various steps required to apply BN structure discovery to animal health data, illustrative analyses of data collected during a previously published study concerned with exposure to bovine viral diarrhoea virus in beef cow-calf herds in Scotland are presented.},\\n bibtype = {article},\\n author = {Lewis, F.I. and Brülisauer, F. and Gunn, G.J.},\\n doi = {10.1016/j.prevetmed.2011.02.003},\\n journal = {Preventive Veterinary Medicine},\\n number = {2}\\n}\",\"author_short\":[\"Lewis,  F.\",\"Brülisauer,  F.\",\"Gunn,  G.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0167587711000341\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"lewis-brlisauer-gunn-structurediscoveryinbayesiannetworksananalyticaltoolforanalysingcomplexanimalhealthdata-2011\",\"role\":\"author\",\"keyword\":[\"Knowledge Discovery\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Classification of fluorescence in situ hybridization images using belief networks\",\"type\":\"article\",\"year\":\"2004\",\"keywords\":\"Belief networks,Fluorescence in situ hybridization (FISH),Image classification,K2 algorithm,Naive Bayesian classifier\",\"pages\":\"1777-1785\",\"volume\":\"25\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0167865504001710\",\"month\":\"12\",\"id\":\"3f97b562-8d2e-32ad-8dbe-3bef87896c76\",\"created\":\"2015-04-12T19:47:10.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning.\",\"bibtype\":\"article\",\"author\":\"Malka, Roy and Lerner, Boaz\",\"doi\":\"10.1016/j.patrec.2004.07.010\",\"journal\":\"Pattern Recognition Letters\",\"number\":\"16\",\"bibtex\":\"@article{\\n title = {Classification of fluorescence in situ hybridization images using belief networks},\\n type = {article},\\n year = {2004},\\n keywords = {Belief networks,Fluorescence in situ hybridization (FISH),Image classification,K2 algorithm,Naive Bayesian classifier},\\n pages = {1777-1785},\\n volume = {25},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0167865504001710},\\n month = {12},\\n id = {3f97b562-8d2e-32ad-8dbe-3bef87896c76},\\n created = {2015-04-12T19:47:10.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning.},\\n bibtype = {article},\\n author = {Malka, Roy and Lerner, Boaz},\\n doi = {10.1016/j.patrec.2004.07.010},\\n journal = {Pattern Recognition Letters},\\n number = {16}\\n}\",\"author_short\":[\"Malka,  R.\",\"Lerner,  B.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0167865504001710\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"malka-lerner-classificationoffluorescenceinsituhybridizationimagesusingbeliefnetworks-2004\",\"role\":\"author\",\"keyword\":[\"Belief networks\",\"Fluorescence in situ hybridization (FISH)\",\"Image classification\",\"K2 algorithm\",\"Naive Bayesian classifier\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach\",\"type\":\"article\",\"year\":\"2009\",\"keywords\":\"Bayesian belief network,Diadema antillarum,Dictyota spp.,Grazing pressure,Macroalgal dynamics,Nutrients,Scaridae\",\"pages\":\"1305-1314\",\"volume\":\"220\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0304380009001525\",\"month\":\"5\",\"id\":\"7499dc3b-15d2-3b2c-8cc1-7623649b11d9\",\"created\":\"2015-04-12T20:17:32.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Macroalgae are a major benthic component of coral reefs and their dynamics influence the resilience of coral reefs to disturbance. However, the relative importance of physical and ecological processes in driving macroalgal dynamics is poorly understood. Here we develop a Bayesian belief network (BBN) model to integrate many of these processes and predict the growth of coral reef macroalgae. Bayesian belief networks use probabilistic relationships rather than deterministic rules to quantify the cause and effect assumptions. The model was developed using both new empirical data and quantified relationships elicited from previous studies. We demonstrate the efficacy of the BBN to predict the dynamics of a common Caribbean macroalgal genus Dictyota. Predictions of the model have an average accuracy of 55% (implying that 55% of the predicted categories of Dictyota cover were assigned to the correct class). Sensitivity analysis suggested that macroalgal dynamics were primarily driven by top–down processes of grazing rather than bottom–up nutrification. BBNs provide a useful framework for modelling complex systems, identifying gaps in our scientific understanding and communicating the complexities of the associated uncertainties in an explicit manner to stakeholders. We anticipate that accuracies will improve as new data are added to the model.\",\"bibtype\":\"article\",\"author\":\"Renken, Henk and Mumby, Peter J.\",\"doi\":\"10.1016/j.ecolmodel.2009.02.022\",\"journal\":\"Ecological Modelling\",\"number\":\"9-10\",\"bibtex\":\"@article{\\n title = {Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach},\\n type = {article},\\n year = {2009},\\n keywords = {Bayesian belief network,Diadema antillarum,Dictyota spp.,Grazing pressure,Macroalgal dynamics,Nutrients,Scaridae},\\n pages = {1305-1314},\\n volume = {220},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380009001525},\\n month = {5},\\n id = {7499dc3b-15d2-3b2c-8cc1-7623649b11d9},\\n created = {2015-04-12T20:17:32.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Macroalgae are a major benthic component of coral reefs and their dynamics influence the resilience of coral reefs to disturbance. However, the relative importance of physical and ecological processes in driving macroalgal dynamics is poorly understood. Here we develop a Bayesian belief network (BBN) model to integrate many of these processes and predict the growth of coral reef macroalgae. Bayesian belief networks use probabilistic relationships rather than deterministic rules to quantify the cause and effect assumptions. The model was developed using both new empirical data and quantified relationships elicited from previous studies. We demonstrate the efficacy of the BBN to predict the dynamics of a common Caribbean macroalgal genus Dictyota. Predictions of the model have an average accuracy of 55% (implying that 55% of the predicted categories of Dictyota cover were assigned to the correct class). Sensitivity analysis suggested that macroalgal dynamics were primarily driven by top–down processes of grazing rather than bottom–up nutrification. BBNs provide a useful framework for modelling complex systems, identifying gaps in our scientific understanding and communicating the complexities of the associated uncertainties in an explicit manner to stakeholders. We anticipate that accuracies will improve as new data are added to the model.},\\n bibtype = {article},\\n author = {Renken, Henk and Mumby, Peter J.},\\n doi = {10.1016/j.ecolmodel.2009.02.022},\\n journal = {Ecological Modelling},\\n number = {9-10}\\n}\",\"author_short\":[\"Renken,  H.\",\"Mumby,  P., J.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0304380009001525\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"renken-mumby-modellingthedynamicsofcoralreefmacroalgaeusingabayesianbeliefnetworkapproach-2009\",\"role\":\"author\",\"keyword\":[\"Bayesian belief network\",\"Diadema antillarum\",\"Dictyota spp.\",\"Grazing pressure\",\"Macroalgal dynamics\",\"Nutrients\",\"Scaridae\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Dynamic Bayesian Networks to predict sequences of organ failures in patients admitted to ICU.\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Aged,Algorithms,Bayes Theorem,Critical Illness,Decision Support Systems, Clinical,Female,Hospital Mortality,Humans,Intensive Care,Intensive Care Units,Intensive Care: methods,Length of Stay,Male,Middle Aged,Multiple Organ Failure,Multiple Organ Failure: physiopathology,Multiple Organ Failure: therapy,Probability,Prognosis,Software,Time Factors\",\"pages\":\"106-13\",\"volume\":\"48\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1532046413001998\",\"month\":\"4\",\"id\":\"009707e8-f54a-399f-95c0-b1de38f6d52a\",\"created\":\"2015-04-12T20:17:32.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Multi Organ Dysfunction Syndrome (MODS) represents a continuum of physiologic derangements and is the major cause of death in the Intensive Care Unit (ICU). Scoring systems for organ failure have become an integral part of critical care practice and play an important role in ICU-based research by tracking disease progression and facilitating patient stratification based on evaluation of illness severity during ICU stay. In this study a Dynamic Bayesian Network (DBN) was applied to model SOFA severity score changes in 79 adult critically ill patients consecutively admitted to the general ICU of the Sant'Andrea University hospital (Rome, Italy) from September 2010 to March 2011, with the aim to identify the most probable sequences of organs failures in the first week after the ICU admission. Approximately 56% of patients were admitted into the ICU with lung failure and about 27% of patients with heart failure. Results suggest that, given the first organ failure at the ICU admission, a sequence of organ failures can be predicted with a certain degree of probability. Sequences involving heart, lung, hematologic system and liver turned out to be the more likely to occur, with slightly different probabilities depending on the day of the week they occur. DBNs could be successfully applied for modeling temporal systems in critical care domain. Capability to predict sequences of likely organ failures makes DBNs a promising prognostic tool, intended to help physicians in undertaking therapeutic decisions in a patient-tailored approach.\",\"bibtype\":\"article\",\"author\":\"Sandri, Micol and Berchialla, Paola and Baldi, Ileana and Gregori, Dario and De Blasi, Roberto Alberto\",\"doi\":\"10.1016/j.jbi.2013.12.008\",\"journal\":\"Journal of biomedical informatics\",\"bibtex\":\"@article{\\n title = {Dynamic Bayesian Networks to predict sequences of organ failures in patients admitted to ICU.},\\n type = {article},\\n year = {2014},\\n keywords = {Aged,Algorithms,Bayes Theorem,Critical Illness,Decision Support Systems, Clinical,Female,Hospital Mortality,Humans,Intensive Care,Intensive Care Units,Intensive Care: methods,Length of Stay,Male,Middle Aged,Multiple Organ Failure,Multiple Organ Failure: physiopathology,Multiple Organ Failure: therapy,Probability,Prognosis,Software,Time Factors},\\n pages = {106-13},\\n volume = {48},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046413001998},\\n month = {4},\\n id = {009707e8-f54a-399f-95c0-b1de38f6d52a},\\n created = {2015-04-12T20:17:32.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Multi Organ Dysfunction Syndrome (MODS) represents a continuum of physiologic derangements and is the major cause of death in the Intensive Care Unit (ICU). Scoring systems for organ failure have become an integral part of critical care practice and play an important role in ICU-based research by tracking disease progression and facilitating patient stratification based on evaluation of illness severity during ICU stay. In this study a Dynamic Bayesian Network (DBN) was applied to model SOFA severity score changes in 79 adult critically ill patients consecutively admitted to the general ICU of the Sant'Andrea University hospital (Rome, Italy) from September 2010 to March 2011, with the aim to identify the most probable sequences of organs failures in the first week after the ICU admission. Approximately 56% of patients were admitted into the ICU with lung failure and about 27% of patients with heart failure. Results suggest that, given the first organ failure at the ICU admission, a sequence of organ failures can be predicted with a certain degree of probability. Sequences involving heart, lung, hematologic system and liver turned out to be the more likely to occur, with slightly different probabilities depending on the day of the week they occur. DBNs could be successfully applied for modeling temporal systems in critical care domain. Capability to predict sequences of likely organ failures makes DBNs a promising prognostic tool, intended to help physicians in undertaking therapeutic decisions in a patient-tailored approach.},\\n bibtype = {article},\\n author = {Sandri, Micol and Berchialla, Paola and Baldi, Ileana and Gregori, Dario and De Blasi, Roberto Alberto},\\n doi = {10.1016/j.jbi.2013.12.008},\\n journal = {Journal of biomedical informatics}\\n}\",\"author_short\":[\"Sandri,  M.\",\"Berchialla,  P.\",\"Baldi,  I.\",\"Gregori,  D.\",\"De Blasi,  R., A.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1532046413001998\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"sandri-berchialla-baldi-gregori-deblasi-dynamicbayesiannetworkstopredictsequencesoforganfailuresinpatientsadmittedtoicu-2014\",\"role\":\"author\",\"keyword\":[\"Aged\",\"Algorithms\",\"Bayes Theorem\",\"Critical Illness\",\"Decision Support Systems\",\"Clinical\",\"Female\",\"Hospital Mortality\",\"Humans\",\"Intensive Care\",\"Intensive Care Units\",\"Intensive Care: methods\",\"Length of Stay\",\"Male\",\"Middle Aged\",\"Multiple Organ Failure\",\"Multiple Organ Failure: physiopathology\",\"Multiple Organ Failure: therapy\",\"Probability\",\"Prognosis\",\"Software\",\"Time Factors\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment.\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Alzheimer Disease,Alzheimer Disease: classification,Alzheimer Disease: diagnosis,Alzheimer Disease: physiopathology,Bayes Theorem,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Mental Disorders,Mental Disorders: classification,Mental Disorders: diagnosis,Mental Disorders: physiopathology,Models, Biological\",\"pages\":\"140-58\",\"volume\":\"51\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0010482514000961\",\"month\":\"8\",\"id\":\"098541ed-8785-3bdd-9a21-5db89d4f8b85\",\"created\":\"2015-04-12T20:17:33.000Z\",\"accessed\":\"2015-04-08\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.\",\"bibtype\":\"article\",\"author\":\"Seixas, Flávio Luiz and Zadrozny, Bianca and Laks, Jerson and Conci, Aura and Muchaluat Saade, Débora Christina\",\"doi\":\"10.1016/j.compbiomed.2014.04.010\",\"journal\":\"Computers in biology and medicine\",\"bibtex\":\"@article{\\n title = {A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment.},\\n type = {article},\\n year = {2014},\\n keywords = {Alzheimer Disease,Alzheimer Disease: classification,Alzheimer Disease: diagnosis,Alzheimer Disease: physiopathology,Bayes Theorem,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Mental Disorders,Mental Disorders: classification,Mental Disorders: diagnosis,Mental Disorders: physiopathology,Models, Biological},\\n pages = {140-58},\\n volume = {51},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482514000961},\\n month = {8},\\n id = {098541ed-8785-3bdd-9a21-5db89d4f8b85},\\n created = {2015-04-12T20:17:33.000Z},\\n accessed = {2015-04-08},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.},\\n bibtype = {article},\\n author = {Seixas, Flávio Luiz and Zadrozny, Bianca and Laks, Jerson and Conci, Aura and Muchaluat Saade, Débora Christina},\\n doi = {10.1016/j.compbiomed.2014.04.010},\\n journal = {Computers in biology and medicine}\\n}\",\"author_short\":[\"Seixas,  F., L.\",\"Zadrozny,  B.\",\"Laks,  J.\",\"Conci,  A.\",\"Muchaluat Saade,  D., C.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0010482514000961\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"seixas-zadrozny-laks-conci-muchaluatsaade-abayesiannetworkdecisionmodelforsupportingthediagnosisofdementiaalzheimersdiseaseandmildcognitiveimpairment-2014\",\"role\":\"author\",\"keyword\":[\"Alzheimer Disease\",\"Alzheimer Disease: classification\",\"Alzheimer Disease: diagnosis\",\"Alzheimer Disease: physiopathology\",\"Bayes Theorem\",\"Diagnosis\",\"Computer-Assisted\",\"Diagnosis\",\"Computer-Assisted: methods\",\"Humans\",\"Mental Disorders\",\"Mental Disorders: classification\",\"Mental Disorders: diagnosis\",\"Mental Disorders: physiopathology\",\"Models\",\"Biological\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Dynamic Bayesian networks as prognostic models for clinical patient management.\",\"type\":\"article\",\"year\":\"2008\",\"keywords\":\"Algorithms,Bayes Theorem,Carcinoid Tumor,Carcinoid Tumor: diagnosis,Carcinoid Tumor: mortality,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Neural Networks (Computer),Prognosis,Risk Assessment,Risk Assessment: methods,Risk Factors,Survival Analysis,Survival Rate\",\"pages\":\"515-29\",\"volume\":\"41\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1532046408000154\",\"month\":\"8\",\"id\":\"ce5b78cb-e69e-3915-8050-fc5b21a5b41c\",\"created\":\"2015-04-12T20:17:33.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Prognostic models in medicine are usually been built using simple decision rules, proportional hazards models, or Markov models. Dynamic Bayesian networks (DBNs) offer an approach that allows for the incorporation of the causal and temporal nature of medical domain knowledge as elicited from domain experts, thereby allowing for detailed prognostic predictions. The aim of this paper is to describe the considerations that must be taken into account when constructing a DBN for complex medical domains and to demonstrate their usefulness in practice. To this end, we focus on the construction of a DBN for prognosis of carcinoid patients, compare performance with that of a proportional hazards model, and describe predictions for three individual patients. We show that the DBN can make detailed predictions, about not only patient survival, but also other variables of interest, such as disease progression, the effect of treatment, and the development of complications. Strengths and limitations of our approach are discussed and compared with those offered by traditional methods.\",\"bibtype\":\"article\",\"author\":\"van Gerven, Marcel A J and Taal, Babs G and Lucas, Peter J F\",\"doi\":\"10.1016/j.jbi.2008.01.006\",\"journal\":\"Journal of biomedical informatics\",\"number\":\"4\",\"bibtex\":\"@article{\\n title = {Dynamic Bayesian networks as prognostic models for clinical patient management.},\\n type = {article},\\n year = {2008},\\n keywords = {Algorithms,Bayes Theorem,Carcinoid Tumor,Carcinoid Tumor: diagnosis,Carcinoid Tumor: mortality,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Neural Networks (Computer),Prognosis,Risk Assessment,Risk Assessment: methods,Risk Factors,Survival Analysis,Survival Rate},\\n pages = {515-29},\\n volume = {41},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046408000154},\\n month = {8},\\n id = {ce5b78cb-e69e-3915-8050-fc5b21a5b41c},\\n created = {2015-04-12T20:17:33.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Prognostic models in medicine are usually been built using simple decision rules, proportional hazards models, or Markov models. Dynamic Bayesian networks (DBNs) offer an approach that allows for the incorporation of the causal and temporal nature of medical domain knowledge as elicited from domain experts, thereby allowing for detailed prognostic predictions. The aim of this paper is to describe the considerations that must be taken into account when constructing a DBN for complex medical domains and to demonstrate their usefulness in practice. To this end, we focus on the construction of a DBN for prognosis of carcinoid patients, compare performance with that of a proportional hazards model, and describe predictions for three individual patients. We show that the DBN can make detailed predictions, about not only patient survival, but also other variables of interest, such as disease progression, the effect of treatment, and the development of complications. Strengths and limitations of our approach are discussed and compared with those offered by traditional methods.},\\n bibtype = {article},\\n author = {van Gerven, Marcel A J and Taal, Babs G and Lucas, Peter J F},\\n doi = {10.1016/j.jbi.2008.01.006},\\n journal = {Journal of biomedical informatics},\\n number = {4}\\n}\",\"author_short\":[\"van Gerven,  M., A., J.\",\"Taal,  B., G.\",\"Lucas,  P., J., F.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1532046408000154\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"vangerven-taal-lucas-dynamicbayesiannetworksasprognosticmodelsforclinicalpatientmanagement-2008\",\"role\":\"author\",\"keyword\":[\"Algorithms\",\"Bayes Theorem\",\"Carcinoid Tumor\",\"Carcinoid Tumor: diagnosis\",\"Carcinoid Tumor: mortality\",\"Decision Support Systems\",\"Clinical\",\"Diagnosis\",\"Computer-Assisted\",\"Diagnosis\",\"Computer-Assisted: methods\",\"Humans\",\"Neural Networks (Computer)\",\"Prognosis\",\"Risk Assessment\",\"Risk Assessment: methods\",\"Risk Factors\",\"Survival Analysis\",\"Survival Rate\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Learning Bayesian networks for clinical time series analysis.\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Aged,Algorithms,Area Under Curve,Artificial Intelligence,Bayes Theorem,Computer Simulation,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Female,Humans,Lung,Lung: physiology,Male,Middle Aged,Monitoring, Ambulatory,Monitoring, Ambulatory: methods,Probability,Pulmonary Disease, Chronic Obstructive,Pulmonary Disease, Chronic Obstructive: therapy,Signal Processing, Computer-Assisted,Time Factors\",\"pages\":\"94-105\",\"volume\":\"48\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1532046413001986\",\"month\":\"4\",\"id\":\"5a113d64-7b66-3d77-88c5-32fbe2e25e03\",\"created\":\"2015-04-12T20:17:34.000Z\",\"accessed\":\"2015-04-07\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"INTRODUCTION: Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status.\\n\\nMETHODS: Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set.\\n\\nRESULTS: The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.\",\"bibtype\":\"article\",\"author\":\"van der Heijden, Maarten and Velikova, Marina and Lucas, Peter J F\",\"doi\":\"10.1016/j.jbi.2013.12.007\",\"journal\":\"Journal of biomedical informatics\",\"bibtex\":\"@article{\\n title = {Learning Bayesian networks for clinical time series analysis.},\\n type = {article},\\n year = {2014},\\n keywords = {Aged,Algorithms,Area Under Curve,Artificial Intelligence,Bayes Theorem,Computer Simulation,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Female,Humans,Lung,Lung: physiology,Male,Middle Aged,Monitoring, Ambulatory,Monitoring, Ambulatory: methods,Probability,Pulmonary Disease, Chronic Obstructive,Pulmonary Disease, Chronic Obstructive: therapy,Signal Processing, Computer-Assisted,Time Factors},\\n pages = {94-105},\\n volume = {48},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046413001986},\\n month = {4},\\n id = {5a113d64-7b66-3d77-88c5-32fbe2e25e03},\\n created = {2015-04-12T20:17:34.000Z},\\n accessed = {2015-04-07},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {INTRODUCTION: Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status.\\n\\nMETHODS: Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set.\\n\\nRESULTS: The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.},\\n bibtype = {article},\\n author = {van der Heijden, Maarten and Velikova, Marina and Lucas, Peter J F},\\n doi = {10.1016/j.jbi.2013.12.007},\\n journal = {Journal of biomedical informatics}\\n}\",\"author_short\":[\"van der Heijden,  M.\",\"Velikova,  M.\",\"Lucas,  P., J., F.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1532046413001986\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"vanderheijden-velikova-lucas-learningbayesiannetworksforclinicaltimeseriesanalysis-2014\",\"role\":\"author\",\"keyword\":[\"Aged\",\"Algorithms\",\"Area Under Curve\",\"Artificial Intelligence\",\"Bayes Theorem\",\"Computer Simulation\",\"Decision Support Systems\",\"Clinical\",\"Diagnosis\",\"Computer-Assisted\",\"Female\",\"Humans\",\"Lung\",\"Lung: physiology\",\"Male\",\"Middle Aged\",\"Monitoring\",\"Ambulatory\",\"Monitoring\",\"Ambulatory: methods\",\"Probability\",\"Pulmonary Disease\",\"Chronic Obstructive\",\"Pulmonary Disease\",\"Chronic Obstructive: therapy\",\"Signal Processing\",\"Computer-Assisted\",\"Time Factors\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Prognostic Bayesian networks I: rationale, learning procedure, and clinical use.\",\"type\":\"article\",\"year\":\"2007\",\"keywords\":\"Algorithms,Artificial Intelligence,Bayes Theorem,Computer Simulation,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Models, Biological,Models, Statistical,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Prognosis\",\"pages\":\"609-18\",\"volume\":\"40\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1532046407000615\",\"month\":\"12\",\"id\":\"3a2e1100-6966-3420-a67d-82df563416d6\",\"created\":\"2015-04-12T20:17:34.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Prognostic models are tools to predict the future outcome of disease and disease treatment, one of the fundamental tasks in clinical medicine. This article presents the prognostic Bayesian network (PBN) as a new type of prognostic model that builds on the Bayesian network methodology, and implements a dynamic, process-oriented view on prognosis. A PBN describes the mutual relationships between variables that come into play during subsequent stages of a care process and a clinical outcome. A dedicated procedure for inducing these networks from clinical data is presented. In this procedure, the network is composed of a collection of local supervised learning models that are recursively learned from the data. The procedure optimizes performance of the network's primary task, outcome prediction, and handles the fact that patients may drop out of the process in earlier stages. Furthermore, the article describes how PBNs can be applied to solve a number of information problems that are related to medical prognosis.\",\"bibtype\":\"article\",\"author\":\"Verduijn, Marion and Peek, Niels and Rosseel, Peter M J and de Jonge, Evert and de Mol, Bas A J M\",\"doi\":\"10.1016/j.jbi.2007.07.003\",\"journal\":\"Journal of biomedical informatics\",\"number\":\"6\",\"bibtex\":\"@article{\\n title = {Prognostic Bayesian networks I: rationale, learning procedure, and clinical use.},\\n type = {article},\\n year = {2007},\\n keywords = {Algorithms,Artificial Intelligence,Bayes Theorem,Computer Simulation,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Models, Biological,Models, Statistical,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Prognosis},\\n pages = {609-18},\\n volume = {40},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046407000615},\\n month = {12},\\n id = {3a2e1100-6966-3420-a67d-82df563416d6},\\n created = {2015-04-12T20:17:34.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Prognostic models are tools to predict the future outcome of disease and disease treatment, one of the fundamental tasks in clinical medicine. This article presents the prognostic Bayesian network (PBN) as a new type of prognostic model that builds on the Bayesian network methodology, and implements a dynamic, process-oriented view on prognosis. A PBN describes the mutual relationships between variables that come into play during subsequent stages of a care process and a clinical outcome. A dedicated procedure for inducing these networks from clinical data is presented. In this procedure, the network is composed of a collection of local supervised learning models that are recursively learned from the data. The procedure optimizes performance of the network's primary task, outcome prediction, and handles the fact that patients may drop out of the process in earlier stages. Furthermore, the article describes how PBNs can be applied to solve a number of information problems that are related to medical prognosis.},\\n bibtype = {article},\\n author = {Verduijn, Marion and Peek, Niels and Rosseel, Peter M J and de Jonge, Evert and de Mol, Bas A J M},\\n doi = {10.1016/j.jbi.2007.07.003},\\n journal = {Journal of biomedical informatics},\\n number = {6}\\n}\",\"author_short\":[\"Verduijn,  M.\",\"Peek,  N.\",\"Rosseel,  P., M., J.\",\"de Jonge,  E.\",\"de Mol,  B., A., J., M.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1532046407000615\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"verduijn-peek-rosseel-dejonge-demol-prognosticbayesiannetworksirationalelearningprocedureandclinicaluse-2007\",\"role\":\"author\",\"keyword\":[\"Algorithms\",\"Artificial Intelligence\",\"Bayes Theorem\",\"Computer Simulation\",\"Decision Support Systems\",\"Clinical\",\"Diagnosis\",\"Computer-Assisted\",\"Diagnosis\",\"Computer-Assisted: methods\",\"Humans\",\"Models\",\"Biological\",\"Models\",\"Statistical\",\"Pattern Recognition\",\"Automated\",\"Pattern Recognition\",\"Automated: methods\",\"Prognosis\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: a case study of Taiwan.\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Aged,Algorithms,Bayes Theorem,Brain Neoplasms,Brain Neoplasms: epidemiology,Brain Neoplasms: secondary,Computational Biology,Computational Biology: methods,Female,Humans,Lung Neoplasms,Lung Neoplasms: epidemiology,Lung Neoplasms: pathology,Male,Middle Aged,Models, Statistical,Sensitivity and Specificity,Taiwan,Taiwan: epidemiology\",\"pages\":\"147-60\",\"volume\":\"47\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S001048251400033X\",\"month\":\"4\",\"id\":\"c853e323-681e-3ae4-80c6-3b543722b7f4\",\"created\":\"2015-04-12T20:17:34.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"The Bayesian network (BN) is a promising method for modeling cancer metastasis under uncertainty. BN is graphically represented using bioinformatics variables and can be used to support an informative medical decision/observation by using probabilistic reasoning. In this study, we propose such a BN to describe and predict the occurrence of brain metastasis from lung cancer. A nationwide database containing more than 50,000 cases of cancer patients from 1996 to 2010 in Taiwan was used in this study. The BN topology for studying brain metastasis from lung cancer was rigorously examined by domain experts/doctors. We used three statistical measures, namely, the accuracy, sensitivity, and specificity, to evaluate the performances of the proposed BN model and to compare it with three competitive approaches, namely, naive Bayes (NB), logistic regression (LR) and support vector machine (SVM). Experimental results show that no significant differences are observed in accuracy or specificity among the four models, while the proposed BN outperforms the others in terms of sampled average sensitivity. Moreover the proposed BN has advantages compared with the other approaches in interpreting how brain metastasis develops from lung cancer. It is shown to be easily understood by physicians, to be efficient in modeling non-linear situations, capable of solving stochastic medical problems, and handling situations wherein information are missing in the context of the occurrence of brain metastasis from lung cancer.\",\"bibtype\":\"article\",\"author\":\"Wang, Kung-Jeng and Makond, Bunjira and Wang, Kung-Min\",\"doi\":\"10.1016/j.compbiomed.2014.02.002\",\"journal\":\"Computers in biology and medicine\",\"bibtex\":\"@article{\\n title = {Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: a case study of Taiwan.},\\n type = {article},\\n year = {2014},\\n keywords = {Aged,Algorithms,Bayes Theorem,Brain Neoplasms,Brain Neoplasms: epidemiology,Brain Neoplasms: secondary,Computational Biology,Computational Biology: methods,Female,Humans,Lung Neoplasms,Lung Neoplasms: epidemiology,Lung Neoplasms: pathology,Male,Middle Aged,Models, Statistical,Sensitivity and Specificity,Taiwan,Taiwan: epidemiology},\\n pages = {147-60},\\n volume = {47},\\n websites = {http://www.sciencedirect.com/science/article/pii/S001048251400033X},\\n month = {4},\\n id = {c853e323-681e-3ae4-80c6-3b543722b7f4},\\n created = {2015-04-12T20:17:34.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The Bayesian network (BN) is a promising method for modeling cancer metastasis under uncertainty. BN is graphically represented using bioinformatics variables and can be used to support an informative medical decision/observation by using probabilistic reasoning. In this study, we propose such a BN to describe and predict the occurrence of brain metastasis from lung cancer. A nationwide database containing more than 50,000 cases of cancer patients from 1996 to 2010 in Taiwan was used in this study. The BN topology for studying brain metastasis from lung cancer was rigorously examined by domain experts/doctors. We used three statistical measures, namely, the accuracy, sensitivity, and specificity, to evaluate the performances of the proposed BN model and to compare it with three competitive approaches, namely, naive Bayes (NB), logistic regression (LR) and support vector machine (SVM). Experimental results show that no significant differences are observed in accuracy or specificity among the four models, while the proposed BN outperforms the others in terms of sampled average sensitivity. Moreover the proposed BN has advantages compared with the other approaches in interpreting how brain metastasis develops from lung cancer. It is shown to be easily understood by physicians, to be efficient in modeling non-linear situations, capable of solving stochastic medical problems, and handling situations wherein information are missing in the context of the occurrence of brain metastasis from lung cancer.},\\n bibtype = {article},\\n author = {Wang, Kung-Jeng and Makond, Bunjira and Wang, Kung-Min},\\n doi = {10.1016/j.compbiomed.2014.02.002},\\n journal = {Computers in biology and medicine}\\n}\",\"author_short\":[\"Wang,  K.\",\"Makond,  B.\",\"Wang,  K.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S001048251400033X\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"wang-makond-wang-modelingandpredictingtheoccurrenceofbrainmetastasisfromlungcancerbybayesiannetworkacasestudyoftaiwan-2014\",\"role\":\"author\",\"keyword\":[\"Aged\",\"Algorithms\",\"Bayes Theorem\",\"Brain Neoplasms\",\"Brain Neoplasms: epidemiology\",\"Brain Neoplasms: secondary\",\"Computational Biology\",\"Computational Biology: methods\",\"Female\",\"Humans\",\"Lung Neoplasms\",\"Lung Neoplasms: epidemiology\",\"Lung Neoplasms: pathology\",\"Male\",\"Middle Aged\",\"Models\",\"Statistical\",\"Sensitivity and Specificity\",\"Taiwan\",\"Taiwan: epidemiology\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Not just data: a method for improving prediction with knowledge.\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Algorithms,Bayes Theorem,Blood Coagulation,Blood Coagulation Disorders,Blood Coagulation Disorders: therapy,Cluster Analysis,Decision Making,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Emergency Medical Services,Emergency Medical Services: organization & adminis,Humans,Medical Errors,Medical Errors: prevention & control,Medical Informatics,Medical Informatics: methods,Medical Informatics: trends,Risk Assessment,Sensitivity and Specificity\",\"pages\":\"28-37\",\"volume\":\"48\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1532046413001640\",\"month\":\"4\",\"id\":\"1d43401a-3c97-343a-b1b0-d9e0f0ab9781\",\"created\":\"2015-04-12T20:17:35.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.\",\"bibtype\":\"article\",\"author\":\"Yet, Barbaros and Perkins, Zane and Fenton, Norman and Tai, Nigel and Marsh, William\",\"doi\":\"10.1016/j.jbi.2013.10.012\",\"journal\":\"Journal of biomedical informatics\",\"bibtex\":\"@article{\\n title = {Not just data: a method for improving prediction with knowledge.},\\n type = {article},\\n year = {2014},\\n keywords = {Algorithms,Bayes Theorem,Blood Coagulation,Blood Coagulation Disorders,Blood Coagulation Disorders: therapy,Cluster Analysis,Decision Making,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Emergency Medical Services,Emergency Medical Services: organization & adminis,Humans,Medical Errors,Medical Errors: prevention & control,Medical Informatics,Medical Informatics: methods,Medical Informatics: trends,Risk Assessment,Sensitivity and Specificity},\\n pages = {28-37},\\n volume = {48},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046413001640},\\n month = {4},\\n id = {1d43401a-3c97-343a-b1b0-d9e0f0ab9781},\\n created = {2015-04-12T20:17:35.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.},\\n bibtype = {article},\\n author = {Yet, Barbaros and Perkins, Zane and Fenton, Norman and Tai, Nigel and Marsh, William},\\n doi = {10.1016/j.jbi.2013.10.012},\\n journal = {Journal of biomedical informatics}\\n}\",\"author_short\":[\"Yet,  B.\",\"Perkins,  Z.\",\"Fenton,  N.\",\"Tai,  N.\",\"Marsh,  W.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1532046413001640\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014\",\"role\":\"author\",\"keyword\":[\"Algorithms\",\"Bayes Theorem\",\"Blood Coagulation\",\"Blood Coagulation Disorders\",\"Blood Coagulation Disorders: therapy\",\"Cluster Analysis\",\"Decision Making\",\"Decision Support Systems\",\"Clinical\",\"Diagnosis\",\"Computer-Assisted\",\"Emergency Medical Services\",\"Emergency Medical Services: organization & adminis\",\"Humans\",\"Medical Errors\",\"Medical Errors: prevention & control\",\"Medical Informatics\",\"Medical Informatics: methods\",\"Medical Informatics: trends\",\"Risk Assessment\",\"Sensitivity and Specificity\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Using a Bayesian belief network to aid differential diagnosis of tropical bovine diseases\",\"type\":\"article\",\"year\":\"2000\",\"keywords\":\"Africa,Bayesian belief network,Cattle disease,Differential diagnosis,Expert system\",\"pages\":\"141-156\",\"volume\":\"47\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0167587700001720\",\"month\":\"11\",\"id\":\"c6fb9f5d-9401-3d6e-a6ec-1fc41a7a7912\",\"created\":\"2015-04-12T20:17:35.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"The examination of presenting signs has always played an important role in the diagnosis of diseases in animal populations. In the case of diseases of tropical cattle, such expertise is often scarce and confined to those experts with many years of experience. To capture, conserve and disseminate such valuable expert knowledge remains a key challenge to the application of knowledge-based systems in veterinary medicine. In this communication, we explore the use of a Bayesian belief network to quantify expert opinion with a view to estimating the likelihood of various diseases in the presence and absence of certain signs. Information was elicited from a panel of 44 experienced veterinarians to provide the response matrix of 27 signs associated with 20 commonly occurring diseases in sub-Saharan cattle. Using this prior information, estimates of the probability of certain signs occurring with each disease were calculated from which the Bayesian belief network was able to propagate the posterior probability of each of the diseases based on the observed signs. The method as an aid in making diagnosis is discussed. It is recognised that such an approach is but one strand in the process of arriving at a diagnosis. For ease of use and accessibility, the approach has been converted into the software program CaDDiS (Cattle Disease Diagnosis System) which is available for consultation on the World Wide Web.\",\"bibtype\":\"article\",\"author\":\"McKendrick, I.J and Gettinby, G and Gu, Y and Reid, S.W.J and Revie, C.W\",\"doi\":\"10.1016/S0167-5877(00)00172-0\",\"journal\":\"Preventive Veterinary Medicine\",\"number\":\"3\",\"bibtex\":\"@article{\\n title = {Using a Bayesian belief network to aid differential diagnosis of tropical bovine diseases},\\n type = {article},\\n year = {2000},\\n keywords = {Africa,Bayesian belief network,Cattle disease,Differential diagnosis,Expert system},\\n pages = {141-156},\\n volume = {47},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587700001720},\\n month = {11},\\n id = {c6fb9f5d-9401-3d6e-a6ec-1fc41a7a7912},\\n created = {2015-04-12T20:17:35.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The examination of presenting signs has always played an important role in the diagnosis of diseases in animal populations. In the case of diseases of tropical cattle, such expertise is often scarce and confined to those experts with many years of experience. To capture, conserve and disseminate such valuable expert knowledge remains a key challenge to the application of knowledge-based systems in veterinary medicine. In this communication, we explore the use of a Bayesian belief network to quantify expert opinion with a view to estimating the likelihood of various diseases in the presence and absence of certain signs. Information was elicited from a panel of 44 experienced veterinarians to provide the response matrix of 27 signs associated with 20 commonly occurring diseases in sub-Saharan cattle. Using this prior information, estimates of the probability of certain signs occurring with each disease were calculated from which the Bayesian belief network was able to propagate the posterior probability of each of the diseases based on the observed signs. The method as an aid in making diagnosis is discussed. It is recognised that such an approach is but one strand in the process of arriving at a diagnosis. For ease of use and accessibility, the approach has been converted into the software program CaDDiS (Cattle Disease Diagnosis System) which is available for consultation on the World Wide Web.},\\n bibtype = {article},\\n author = {McKendrick, I.J and Gettinby, G and Gu, Y and Reid, S.W.J and Revie, C.W},\\n doi = {10.1016/S0167-5877(00)00172-0},\\n journal = {Preventive Veterinary Medicine},\\n number = {3}\\n}\",\"author_short\":[\"McKendrick,  I.\",\"Gettinby,  G.\",\"Gu,  Y.\",\"Reid,  S.\",\"Revie,  C.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0167587700001720\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"mckendrick-gettinby-gu-reid-revie-usingabayesianbeliefnetworktoaiddifferentialdiagnosisoftropicalbovinediseases-2000\",\"role\":\"author\",\"keyword\":[\"Africa\",\"Bayesian belief network\",\"Cattle disease\",\"Differential diagnosis\",\"Expert system\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index\",\"type\":\"article\",\"year\":\"2013\",\"keywords\":\"Bayesian network,Early stage entrepreneurial activity Index,Forecasting,GEM\",\"pages\":\"4003-4009\",\"volume\":\"40\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0957417413000122\",\"month\":\"8\",\"id\":\"4f4aa7b1-b0f6-3c9a-a260-cb8d1af6a8a8\",\"created\":\"2015-04-16T00:39:20.000Z\",\"accessed\":\"2015-02-14\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Entrepreneurship plays a critical role for the development and well-being of society. Illustration of its dynamic relationship with entrepreneurial attitudes and aspirations can provide a guideline for the cause of such activities. However, a time-lagged causal relationship among these concepts has not yet been established. In this study, we examine a dynamic relationship among early stage entrepreneurial attitudes, activities, and aspirations using Bayesian network (BN) analysis. In addition, we propose an early stage entrepreneurial activity index that can predict the percentage of both nascent entrepreneur and new business owner using the variables related to entrepreneurial attitudes of the previous year. This index, in turn, can be used to predict various aspects of entrepreneurial aspiration of the following year. The proposed index turns out to have very high prediction accuracy and is expected to provide effective policies to boost future entrepreneurial activity and aspiration.\",\"bibtype\":\"article\",\"author\":\"Sohn, So Young and Lee, Ann Sung\",\"doi\":\"10.1016/j.eswa.2013.01.009\",\"journal\":\"Expert Systems with Applications\",\"number\":\"10\",\"bibtex\":\"@article{\\n title = {Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index},\\n type = {article},\\n year = {2013},\\n keywords = {Bayesian network,Early stage entrepreneurial activity Index,Forecasting,GEM},\\n pages = {4003-4009},\\n volume = {40},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417413000122},\\n month = {8},\\n id = {4f4aa7b1-b0f6-3c9a-a260-cb8d1af6a8a8},\\n created = {2015-04-16T00:39:20.000Z},\\n accessed = {2015-02-14},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Entrepreneurship plays a critical role for the development and well-being of society. Illustration of its dynamic relationship with entrepreneurial attitudes and aspirations can provide a guideline for the cause of such activities. However, a time-lagged causal relationship among these concepts has not yet been established. In this study, we examine a dynamic relationship among early stage entrepreneurial attitudes, activities, and aspirations using Bayesian network (BN) analysis. In addition, we propose an early stage entrepreneurial activity index that can predict the percentage of both nascent entrepreneur and new business owner using the variables related to entrepreneurial attitudes of the previous year. This index, in turn, can be used to predict various aspects of entrepreneurial aspiration of the following year. The proposed index turns out to have very high prediction accuracy and is expected to provide effective policies to boost future entrepreneurial activity and aspiration.},\\n bibtype = {article},\\n author = {Sohn, So Young and Lee, Ann Sung},\\n doi = {10.1016/j.eswa.2013.01.009},\\n journal = {Expert Systems with Applications},\\n number = {10}\\n}\",\"author_short\":[\"Sohn,  S., Y.\",\"Lee,  A., S.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0957417413000122\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"sohn-lee-bayesiannetworkanalysisforthedynamicpredictionofearlystageentrepreneurialactivityindex-2013\",\"role\":\"author\",\"keyword\":[\"Bayesian network\",\"Early stage entrepreneurial activity Index\",\"Forecasting\",\"GEM\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A driver fatigue recognition model based on information fusion and dynamic Bayesian network\",\"type\":\"article\",\"year\":\"2010\",\"keywords\":\"Contextual features,Driver fatigue recognition,Dynamic Bayesian network,Information fusion,Physiological features\",\"pages\":\"1942-1954\",\"volume\":\"180\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0020025510000253\",\"month\":\"5\",\"id\":\"af17e830-7378-3917-9df9-516b00560306\",\"created\":\"2015-04-17T15:30:41.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver.\",\"bibtype\":\"article\",\"author\":\"Yang, Guosheng and Lin, Yingzi and Bhattacharya, Prabir\",\"doi\":\"10.1016/j.ins.2010.01.011\",\"journal\":\"Information Sciences\",\"number\":\"10\",\"bibtex\":\"@article{\\n title = {A driver fatigue recognition model based on information fusion and dynamic Bayesian network},\\n type = {article},\\n year = {2010},\\n keywords = {Contextual features,Driver fatigue recognition,Dynamic Bayesian network,Information fusion,Physiological features},\\n pages = {1942-1954},\\n volume = {180},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0020025510000253},\\n month = {5},\\n id = {af17e830-7378-3917-9df9-516b00560306},\\n created = {2015-04-17T15:30:41.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver.},\\n bibtype = {article},\\n author = {Yang, Guosheng and Lin, Yingzi and Bhattacharya, Prabir},\\n doi = {10.1016/j.ins.2010.01.011},\\n journal = {Information Sciences},\\n number = {10}\\n}\",\"author_short\":[\"Yang,  G.\",\"Lin,  Y.\",\"Bhattacharya,  P.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0020025510000253\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"yang-lin-bhattacharya-adriverfatiguerecognitionmodelbasedoninformationfusionanddynamicbayesiannetwork-2010\",\"role\":\"author\",\"keyword\":[\"Contextual features\",\"Driver fatigue recognition\",\"Dynamic Bayesian network\",\"Information fusion\",\"Physiological features\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Bayesian network,Development,Habitat,Piping plover,Sea-level rise,Shorebird\",\"pages\":\"38-50\",\"volume\":\"276\",\"websites\":\"https://darchive.mblwhoilibrary.org/handle/1912/7233\",\"month\":\"3\",\"publisher\":\"Elsevier\",\"day\":\"31\",\"id\":\"18bdf9c6-a58f-3f8a-8215-c2758447ea1d\",\"created\":\"2015-05-07T19:12:09.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2017-03-14T14:38:49.606Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"language\":\"en_US\",\"private_publication\":false,\"abstract\":\"© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Modelling 276 (2014): 38–50, doi:10.1016/j.ecolmodel.2014.01.005.\",\"bibtype\":\"article\",\"author\":\"Gieder, Katherina D. and Karpanty, Sarah M. and Fraser, James D. and Catlin, Daniel H. and Gutierrez, Benjamin T. and Plant, Nathaniel G. and Turecek, Aaron M. and Robert Thieler, E.\",\"doi\":\"10.1016/j.ecolmodel.2014.01.005\",\"journal\":\"Ecological Modelling\",\"bibtex\":\"@article{\\n title = {A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features},\\n type = {article},\\n year = {2014},\\n keywords = {Bayesian network,Development,Habitat,Piping plover,Sea-level rise,Shorebird},\\n pages = {38-50},\\n volume = {276},\\n websites = {https://darchive.mblwhoilibrary.org/handle/1912/7233},\\n month = {3},\\n publisher = {Elsevier},\\n day = {31},\\n id = {18bdf9c6-a58f-3f8a-8215-c2758447ea1d},\\n created = {2015-05-07T19:12:09.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2017-03-14T14:38:49.606Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n language = {en_US},\\n private_publication = {false},\\n abstract = {© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Modelling 276 (2014): 38–50, doi:10.1016/j.ecolmodel.2014.01.005.},\\n bibtype = {article},\\n author = {Gieder, Katherina D. and Karpanty, Sarah M. and Fraser, James D. and Catlin, Daniel H. and Gutierrez, Benjamin T. and Plant, Nathaniel G. and Turecek, Aaron M. and Robert Thieler, E.},\\n doi = {10.1016/j.ecolmodel.2014.01.005},\\n journal = {Ecological Modelling}\\n}\",\"author_short\":[\"Gieder,  K., D.\",\"Karpanty,  S., M.\",\"Fraser,  J., D.\",\"Catlin,  D., H.\",\"Gutierrez,  B., T.\",\"Plant,  N., G.\",\"Turecek,  A., M.\",\"Robert Thieler,  E.\"],\"urls\":{\"Website\":\"https://darchive.mblwhoilibrary.org/handle/1912/7233\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014\",\"role\":\"author\",\"keyword\":[\"Bayesian network\",\"Development\",\"Habitat\",\"Piping plover\",\"Sea-level rise\",\"Shorebird\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis\",\"type\":\"article\",\"year\":\"2018\",\"websites\":\"http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf\",\"id\":\"5b5c1742-115d-3d0d-9205-dae3a9e85e95\",\"created\":\"2018-03-31T21:53:37.316Z\",\"accessed\":\"2018-03-31\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2018-03-31T23:54:10.489Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"— The aim of the investigation was to analyse patient clinical data of Bronchiectasis patients in a Singaporean cohort in order to identify any unknown correlations between clinical parameters such as age, sex, presence of certain organisms in body etc. Both supervised learning and unsupervised machine learning computer algorithms were applied to the data set. A strong inverse correlation was found between the A.fumigatus burden level and P.aeruginosa status in the pulmonary track of patients. Differences in cytokine expressions in the body, which were found to be statistically significant, were also observed, and this could be used as a more accurate biomarker to flag Aspergillus as compared to the poorly accurate culture test which is currently used. Future research needs to be conducted to further investigate the relationship between A.fumigatus and P.aeruginosa and the viability of using the identified cytokines as biomarkers.\",\"bibtype\":\"article\",\"author\":\"Venkatesh, Tejas\",\"bibtex\":\"@article{\\n title = {Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis},\\n type = {article},\\n year = {2018},\\n websites = {http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf},\\n id = {5b5c1742-115d-3d0d-9205-dae3a9e85e95},\\n created = {2018-03-31T21:53:37.316Z},\\n accessed = {2018-03-31},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2018-03-31T23:54:10.489Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {— The aim of the investigation was to analyse patient clinical data of Bronchiectasis patients in a Singaporean cohort in order to identify any unknown correlations between clinical parameters such as age, sex, presence of certain organisms in body etc. Both supervised learning and unsupervised machine learning computer algorithms were applied to the data set. A strong inverse correlation was found between the A.fumigatus burden level and P.aeruginosa status in the pulmonary track of patients. Differences in cytokine expressions in the body, which were found to be statistically significant, were also observed, and this could be used as a more accurate biomarker to flag Aspergillus as compared to the poorly accurate culture test which is currently used. Future research needs to be conducted to further investigate the relationship between A.fumigatus and P.aeruginosa and the viability of using the identified cytokines as biomarkers.},\\n bibtype = {article},\\n author = {Venkatesh, Tejas}\\n}\",\"author_short\":[\"Venkatesh,  T.\"],\"urls\":{\"Website\":\"http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis-2018\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Reverse deduction of vehicle group situation based on dynamic Bayesian network\",\"type\":\"article\",\"year\":\"2018\",\"keywords\":\"Poisson's distribution,Vehicle group situation,dynamic Bayesian network,privacy protection,reverse deduction\",\"pages\":\"1-15\",\"volume\":\"10\",\"websites\":\"http://journals.sagepub.com/doi/pdf/10.1177/1687814017747708\",\"id\":\"fd96fbef-7906-3746-a462-6a5cb1829191\",\"created\":\"2018-03-31T23:32:39.494Z\",\"accessed\":\"2018-03-31\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2018-03-31T23:54:10.482Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Vehicle group is the basic unit of microscopic traffic flow, and also a concept that often involved in the research of active vehicle security. It is of great significance to identify vehicle group situation accurately for the research of traffic flow the-ory and the intelligent vehicle driving system. Three-lane condition was taken as an example and the privacy protection of driver (only the data of travel time were used) was a premise in this article. Poisson's distribution was used to identify vehicle group situation which was constituted by target vehicle and its neighboring vehicles when the target vehicle arrived at the end of study area. And the dynamic Bayesian network was used to build the reverse deduction model of vehicle group situation. The model was verified through actual and virtual driving experiments. Verification results showed that the model established in this article was reasonable and feasible.\",\"bibtype\":\"article\",\"author\":\"Wang, Xiaoyuan and Wang, Jianqiang and Liu, Yaqi and Liu, Zhenxue and Wang, Jingheng\",\"doi\":\"10.1177/1687814017747708\",\"journal\":\"Special Issue Article Advances in Mechanical Engineering\",\"number\":\"3\",\"bibtex\":\"@article{\\n title = {Reverse deduction of vehicle group situation based on dynamic Bayesian network},\\n type = {article},\\n year = {2018},\\n keywords = {Poisson's distribution,Vehicle group situation,dynamic Bayesian network,privacy protection,reverse deduction},\\n pages = {1-15},\\n volume = {10},\\n websites = {http://journals.sagepub.com/doi/pdf/10.1177/1687814017747708},\\n id = {fd96fbef-7906-3746-a462-6a5cb1829191},\\n created = {2018-03-31T23:32:39.494Z},\\n accessed = {2018-03-31},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2018-03-31T23:54:10.482Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Vehicle group is the basic unit of microscopic traffic flow, and also a concept that often involved in the research of active vehicle security. It is of great significance to identify vehicle group situation accurately for the research of traffic flow the-ory and the intelligent vehicle driving system. Three-lane condition was taken as an example and the privacy protection of driver (only the data of travel time were used) was a premise in this article. Poisson's distribution was used to identify vehicle group situation which was constituted by target vehicle and its neighboring vehicles when the target vehicle arrived at the end of study area. And the dynamic Bayesian network was used to build the reverse deduction model of vehicle group situation. The model was verified through actual and virtual driving experiments. Verification results showed that the model established in this article was reasonable and feasible.},\\n bibtype = {article},\\n author = {Wang, Xiaoyuan and Wang, Jianqiang and Liu, Yaqi and Liu, Zhenxue and Wang, Jingheng},\\n doi = {10.1177/1687814017747708},\\n journal = {Special Issue Article Advances in Mechanical Engineering},\\n number = {3}\\n}\",\"author_short\":[\"Wang,  X.\",\"Wang,  J.\",\"Liu,  Y.\",\"Liu,  Z.\",\"Wang,  J.\"],\"urls\":{\"Website\":\"http://journals.sagepub.com/doi/pdf/10.1177/1687814017747708\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"wang-wang-liu-liu-wang-reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork-2018\",\"role\":\"author\",\"keyword\":[\"Poisson's distribution\",\"Vehicle group situation\",\"dynamic Bayesian network\",\"privacy protection\",\"reverse deduction\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Analysis and prediction of student placement for improving the education standards\",\"type\":\"article\",\"year\":\"2018\",\"keywords\":\"academics,association rules,decision tree,j48 algorithm,placement,prediction,universities\",\"pages\":\"303-306\",\"volume\":\"7\",\"id\":\"75742eca-76db-39d0-b3ba-ea4502c587e8\",\"created\":\"2018-03-31T23:54:09.395Z\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2018-03-31T23:54:09.395Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Devi, S Anjali and Priya, M Vishnu and Akhila, P and Vasundhara, N\",\"bibtex\":\"@article{\\n title = {Analysis and prediction of student placement for improving the education standards},\\n type = {article},\\n year = {2018},\\n keywords = {academics,association rules,decision tree,j48 algorithm,placement,prediction,universities},\\n pages = {303-306},\\n volume = {7},\\n id = {75742eca-76db-39d0-b3ba-ea4502c587e8},\\n created = {2018-03-31T23:54:09.395Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2018-03-31T23:54:09.395Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Devi, S Anjali and Priya, M Vishnu and Akhila, P and Vasundhara, N}\\n}\",\"author_short\":[\"Devi,  S., A.\",\"Priya,  M., V.\",\"Akhila,  P.\",\"Vasundhara,  N.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"devi-priya-akhila-vasundhara-analysisandpredictionofstudentplacementforimprovingtheeducationstandards-2018\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"academics\",\"association rules\",\"decision tree\",\"j48 algorithm\",\"placement\",\"prediction\",\"universities\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials\",\"type\":\"article\",\"year\":\"2018\",\"keywords\":\"Mechanical properties,Metals and alloys\",\"pages\":\"35\",\"volume\":\"4\",\"websites\":\"http://www.nature.com/articles/s41524-018-0094-7\",\"month\":\"12\",\"publisher\":\"Nature Publishing Group\",\"day\":\"16\",\"id\":\"dc81e604-655d-3c91-a4b7-d70394916669\",\"created\":\"2018-07-23T01:03:03.039Z\",\"accessed\":\"2018-07-22\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2018-07-23T01:03:03.039Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature. Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics.\",\"bibtype\":\"article\",\"author\":\"Rovinelli, Andrea and Sangid, Michael D. and Proudhon, Henry and Ludwig, Wolfgang\",\"doi\":\"10.1038/s41524-018-0094-7\",\"journal\":\"npj Computational Materials\",\"number\":\"1\",\"bibtex\":\"@article{\\n title = {Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials},\\n type = {article},\\n year = {2018},\\n keywords = {Mechanical properties,Metals and alloys},\\n pages = {35},\\n volume = {4},\\n websites = {http://www.nature.com/articles/s41524-018-0094-7},\\n month = {12},\\n publisher = {Nature Publishing Group},\\n day = {16},\\n id = {dc81e604-655d-3c91-a4b7-d70394916669},\\n created = {2018-07-23T01:03:03.039Z},\\n accessed = {2018-07-22},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2018-07-23T01:03:03.039Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature. Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics.},\\n bibtype = {article},\\n author = {Rovinelli, Andrea and Sangid, Michael D. and Proudhon, Henry and Ludwig, Wolfgang},\\n doi = {10.1038/s41524-018-0094-7},\\n journal = {npj Computational Materials},\\n number = {1}\\n}\",\"author_short\":[\"Rovinelli,  A.\",\"Sangid,  M., D.\",\"Proudhon,  H.\",\"Ludwig,  W.\"],\"urls\":{\"Website\":\"http://www.nature.com/articles/s41524-018-0094-7\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"rovinelli-sangid-proudhon-ludwig-usingmachinelearningandadatadrivenapproachtoidentifythesmallfatiguecrackdrivingforceinpolycrystallinematerials-2018\",\"role\":\"author\",\"keyword\":[\"Mechanical properties\",\"Metals and alloys\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Prediction of drivers and pedestrians' behaviors at signalized mid-block Danish offset crosswalks using Bayesian networks\",\"type\":\"article\",\"year\":\"2019\",\"pages\":\"75-83\",\"volume\":\"69\",\"websites\":\"https://www.sciencedirect.com/science/article/pii/S0022437518306595?dgcid=author#!\",\"month\":\"6\",\"publisher\":\"Pergamon\",\"day\":\"1\",\"id\":\"0b551964-6737-3715-9f19-89171b033552\",\"created\":\"2019-03-23T04:04:22.832Z\",\"accessed\":\"2019-03-23\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2019-03-23T04:04:22.918Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"Introduction: This study presents the prediction of driver yielding compliance and pedestrian tendencies to press pushbuttons at signalized mid-block Danish offset crosswalks. Method: It applies Bayesian Networks (BNs) analysis, which is basically a graphical non-functional form model, on observational survey data collected from five signalized crosswalks in Las Vegas, Nevada. The BNs structures were found in the data by applying several score functions. By considering prediction accuracy and the Area under the Receiver Operating Characteristic (ROC) curves, the BN found that using the Bayesian Information Criterion (BIC) score resulted in the best network structure, compared to the ones found using K2 and the Akaike Information Criterion (AIC). The BIC score-based structure was then used for parameter learning and probabilistic inference. Results: Results show that, when considering an individual scenario, the highest predicted yielding compliance (81%) is attained when pedestrians arrive at the crosswalk while the flashes are active, whereas the lowest predicted yielding compliance (23.4%) is observed when the pedestrians cross between the yield line and advanced pedestrian crosswalk sign. On the other hand, crossing within marked stripes, approaching the crosswalk from the near side of the pushbutton pole, inactive flashing lights, and being the first to arrive at the crosswalk result in relatively high-predicted probabilities of pedestrians pressing pushbutton. Furthermore, with a combination of scenarios, the maximum achievable predicted yielding probability is 87.5%, while that of pressing the button was 96.3%. Practical applications: Traffic engineers and planners may use these findings to improve the safety of crosswalk users.\",\"bibtype\":\"article\",\"author\":\"Kutela, Boniphace and Teng, Hualiang\",\"doi\":\"10.1016/J.JSR.2019.02.008\",\"journal\":\"Journal of Safety Research\",\"bibtex\":\"@article{\\n title = {Prediction of drivers and pedestrians' behaviors at signalized mid-block Danish offset crosswalks using Bayesian networks},\\n type = {article},\\n year = {2019},\\n pages = {75-83},\\n volume = {69},\\n websites = {https://www.sciencedirect.com/science/article/pii/S0022437518306595?dgcid=author#!},\\n month = {6},\\n publisher = {Pergamon},\\n day = {1},\\n id = {0b551964-6737-3715-9f19-89171b033552},\\n created = {2019-03-23T04:04:22.832Z},\\n accessed = {2019-03-23},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2019-03-23T04:04:22.918Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Introduction: This study presents the prediction of driver yielding compliance and pedestrian tendencies to press pushbuttons at signalized mid-block Danish offset crosswalks. Method: It applies Bayesian Networks (BNs) analysis, which is basically a graphical non-functional form model, on observational survey data collected from five signalized crosswalks in Las Vegas, Nevada. The BNs structures were found in the data by applying several score functions. By considering prediction accuracy and the Area under the Receiver Operating Characteristic (ROC) curves, the BN found that using the Bayesian Information Criterion (BIC) score resulted in the best network structure, compared to the ones found using K2 and the Akaike Information Criterion (AIC). The BIC score-based structure was then used for parameter learning and probabilistic inference. Results: Results show that, when considering an individual scenario, the highest predicted yielding compliance (81%) is attained when pedestrians arrive at the crosswalk while the flashes are active, whereas the lowest predicted yielding compliance (23.4%) is observed when the pedestrians cross between the yield line and advanced pedestrian crosswalk sign. On the other hand, crossing within marked stripes, approaching the crosswalk from the near side of the pushbutton pole, inactive flashing lights, and being the first to arrive at the crosswalk result in relatively high-predicted probabilities of pedestrians pressing pushbutton. Furthermore, with a combination of scenarios, the maximum achievable predicted yielding probability is 87.5%, while that of pressing the button was 96.3%. Practical applications: Traffic engineers and planners may use these findings to improve the safety of crosswalk users.},\\n bibtype = {article},\\n author = {Kutela, Boniphace and Teng, Hualiang},\\n doi = {10.1016/J.JSR.2019.02.008},\\n journal = {Journal of Safety Research}\\n}\",\"author_short\":[\"Kutela,  B.\",\"Teng,  H.\"],\"urls\":{\"Website\":\"https://www.sciencedirect.com/science/article/pii/S0022437518306595?dgcid=author#!\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"kutela-teng-predictionofdriversandpedestriansbehaviorsatsignalizedmidblockdanishoffsetcrosswalksusingbayesiannetworks-2019\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes\",\"type\":\"article\",\"year\":\"2019\",\"pages\":\"46\",\"volume\":\"3\",\"websites\":\"https://www.mdpi.com/2504-2289/3/3/46\",\"month\":\"7\",\"day\":\"31\",\"id\":\"3f7937c2-97f5-3237-8dc9-009449e1e600\",\"created\":\"2019-08-03T12:08:10.546Z\",\"accessed\":\"2019-08-03\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2019-08-03T12:08:10.635Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"<p>Artificial intelligence-enabled adaptive learning systems (AI-ALS) have been increasingly utilized in education. Schools are usually afforded the freedom to deploy the AI-ALS that they prefer. However, even before artificial intelligence autonomously develops into artificial superintelligence in the future, it would be remiss to entirely leave the students to the AI-ALS without any independent oversight of the potential issues. For example, if the students score well in formative assessments within the AI-ALS but subsequently perform badly in paper-based post-tests, or if the relentless algorithm of a particular AI-ALS is suspected of causing undue stress for the students, they should be addressed by educational stakeholders. Policy makers and educational stakeholders should collaborate to analyze the data from multiple AI-ALS deployed in different schools to achieve strategic oversight. The current paper provides exemplars to illustrate how this future-ready strategic oversight could be implemented using an artificial intelligence-based Bayesian network software to analyze the data from five dissimilar AI-ALS, each deployed in a different school. Besides using descriptive analytics to reveal potential issues experienced by students within each AI-ALS, this human-centric AI-empowered approach also enables explainable predictive analytics of the students’ learning outcomes in paper-based summative assessments after training is completed in each AI-ALS.</p>\",\"bibtype\":\"article\",\"author\":\"HOW, undefined\",\"doi\":\"10.3390/bdcc3030046\",\"journal\":\"Big Data and Cognitive Computing\",\"number\":\"3\",\"bibtex\":\"@article{\\n title = {Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes},\\n type = {article},\\n year = {2019},\\n pages = {46},\\n volume = {3},\\n websites = {https://www.mdpi.com/2504-2289/3/3/46},\\n month = {7},\\n day = {31},\\n id = {3f7937c2-97f5-3237-8dc9-009449e1e600},\\n created = {2019-08-03T12:08:10.546Z},\\n accessed = {2019-08-03},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2019-08-03T12:08:10.635Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {<p>Artificial intelligence-enabled adaptive learning systems (AI-ALS) have been increasingly utilized in education. Schools are usually afforded the freedom to deploy the AI-ALS that they prefer. However, even before artificial intelligence autonomously develops into artificial superintelligence in the future, it would be remiss to entirely leave the students to the AI-ALS without any independent oversight of the potential issues. For example, if the students score well in formative assessments within the AI-ALS but subsequently perform badly in paper-based post-tests, or if the relentless algorithm of a particular AI-ALS is suspected of causing undue stress for the students, they should be addressed by educational stakeholders. Policy makers and educational stakeholders should collaborate to analyze the data from multiple AI-ALS deployed in different schools to achieve strategic oversight. The current paper provides exemplars to illustrate how this future-ready strategic oversight could be implemented using an artificial intelligence-based Bayesian network software to analyze the data from five dissimilar AI-ALS, each deployed in a different school. Besides using descriptive analytics to reveal potential issues experienced by students within each AI-ALS, this human-centric AI-empowered approach also enables explainable predictive analytics of the students’ learning outcomes in paper-based summative assessments after training is completed in each AI-ALS.</p>},\\n bibtype = {article},\\n author = {HOW, undefined},\\n doi = {10.3390/bdcc3030046},\\n journal = {Big Data and Cognitive Computing},\\n number = {3}\\n}\",\"author_short\":[\"HOW\"],\"urls\":{\"Website\":\"https://www.mdpi.com/2504-2289/3/3/46\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"how-futurereadystrategicoversightofmultipleartificialsuperintelligenceenabledadaptivelearningsystemsviahumancentricexplainableaiempoweredpredictiveoptimizationsofeducationaloutcomes-2019\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2020\",\"pages\":\"629-640\",\"websites\":\"http://link.springer.com/10.1007/978-981-13-8676-3_53\",\"publisher\":\"Springer, Singapore\",\"id\":\"748c9768-dd58-364d-b98e-a054acf6f2ec\",\"created\":\"2019-08-22T13:44:38.849Z\",\"accessed\":\"2019-08-22\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2019-08-22T13:44:38.950Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inbook\",\"author\":\"Pati, Dakshya P. and Panda, Sucheta\",\"doi\":\"10.1007/978-981-13-8676-3_53\",\"chapter\":\"A Comprehensive Review on Cancer Detection and Prediction Using Computational Methods\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2020},\\n pages = {629-640},\\n websites = {http://link.springer.com/10.1007/978-981-13-8676-3_53},\\n publisher = {Springer, Singapore},\\n id = {748c9768-dd58-364d-b98e-a054acf6f2ec},\\n created = {2019-08-22T13:44:38.849Z},\\n accessed = {2019-08-22},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2019-08-22T13:44:38.950Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inbook},\\n author = {Pati, Dakshya P. and Panda, Sucheta},\\n doi = {10.1007/978-981-13-8676-3_53},\\n chapter = {A Comprehensive Review on Cancer Detection and Prediction Using Computational Methods}\\n}\",\"author_short\":[\"Pati,  D., P.\",\"Panda,  S.\"],\"urls\":{\"Website\":\"http://link.springer.com/10.1007/978-981-13-8676-3_53\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"pati-panda--2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"title\":\"A Bayesian Network Model for Real-time Crash Prediction Based on Selected Variables by Random Forest\",\"type\":\"inproceedings\",\"year\":\"2019\",\"pages\":\"670-677\",\"websites\":\"https://ieeexplore.ieee.org/document/8883694/\",\"month\":\"7\",\"publisher\":\"IEEE\",\"id\":\"6243ec4c-b668-3702-9e59-cf0cd1279f02\",\"created\":\"2019-11-06T23:01:57.995Z\",\"accessed\":\"2019-11-06\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2019-11-06T23:01:57.995Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Wu, Mingxian and Shan, Donghui and Wang, Zuo and Sun, Xiaoduan and Liu, Jianbei and Sun, Ming\",\"doi\":\"10.1109/ICTIS.2019.8883694\",\"booktitle\":\"2019 5th International Conference on Transportation Information and Safety (ICTIS)\",\"bibtex\":\"@inproceedings{\\n title = {A Bayesian Network Model for Real-time Crash Prediction Based on Selected Variables by Random Forest},\\n type = {inproceedings},\\n year = {2019},\\n pages = {670-677},\\n websites = {https://ieeexplore.ieee.org/document/8883694/},\\n month = {7},\\n publisher = {IEEE},\\n id = {6243ec4c-b668-3702-9e59-cf0cd1279f02},\\n created = {2019-11-06T23:01:57.995Z},\\n accessed = {2019-11-06},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2019-11-06T23:01:57.995Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Wu, Mingxian and Shan, Donghui and Wang, Zuo and Sun, Xiaoduan and Liu, Jianbei and Sun, Ming},\\n doi = {10.1109/ICTIS.2019.8883694},\\n booktitle = {2019 5th International Conference on Transportation Information and Safety (ICTIS)}\\n}\",\"author_short\":[\"Wu,  M.\",\"Shan,  D.\",\"Wang,  Z.\",\"Sun,  X.\",\"Liu,  J.\",\"Sun,  M.\"],\"urls\":{\"Website\":\"https://ieeexplore.ieee.org/document/8883694/\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"wu-shan-wang-sun-liu-sun-abayesiannetworkmodelforrealtimecrashpredictionbasedonselectedvariablesbyrandomforest-2019\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Bayesian Network Classifiers\",\"type\":\"article\",\"year\":\"1997\",\"pages\":\"131-163\",\"volume\":\"29\",\"websites\":\"http://dx.doi.org/10.1023/A:1007465528199%5Cnpapers2://publication/doi/10.1023/A:1007465528199\",\"id\":\"46fa1577-f67d-3e80-bbdd-4bccc555eee0\",\"created\":\"2019-12-27T22:41:19.683Z\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2019-12-27T22:41:19.683Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4. 5. This fact raises the question of ...\",\"bibtype\":\"article\",\"author\":\"Friedman, Nir and Geiger, Dan and Goldszmidt, Moises\",\"doi\":\"10.1023/A:1007465528199\",\"journal\":\"Machine learning\",\"bibtex\":\"@article{\\n title = {Bayesian Network Classifiers},\\n type = {article},\\n year = {1997},\\n pages = {131-163},\\n volume = {29},\\n websites = {http://dx.doi.org/10.1023/A:1007465528199%5Cnpapers2://publication/doi/10.1023/A:1007465528199},\\n id = {46fa1577-f67d-3e80-bbdd-4bccc555eee0},\\n created = {2019-12-27T22:41:19.683Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2019-12-27T22:41:19.683Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4. 5. This fact raises the question of ...},\\n bibtype = {article},\\n author = {Friedman, Nir and Geiger, Dan and Goldszmidt, Moises},\\n doi = {10.1023/A:1007465528199},\\n journal = {Machine learning}\\n}\",\"author_short\":[\"Friedman,  N.\",\"Geiger,  D.\",\"Goldszmidt,  M.\"],\"urls\":{\"Website\":\"http://dx.doi.org/10.1023/A:1007465528199%5Cnpapers2://publication/doi/10.1023/A:1007465528199\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"friedman-geiger-goldszmidt-bayesiannetworkclassifiers-1997\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Estimation and Application of a Bayesian Network Model for Discrete Travel Choice Analysis\",\"type\":\"article\",\"id\":\"56632ecf-ec53-36d3-9c3d-19ed3edf4ce4\",\"created\":\"2020-01-06T20:18:13.290Z\",\"accessed\":\"2020-01-06\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2020-01-06T20:18:13.378Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Xie, Chi\",\"journal\":\"Transportation Letters\",\"bibtex\":\"@article{\\n title = {Estimation and Application of a Bayesian Network Model for Discrete Travel Choice Analysis},\\n type = {article},\\n id = {56632ecf-ec53-36d3-9c3d-19ed3edf4ce4},\\n created = {2020-01-06T20:18:13.290Z},\\n accessed = {2020-01-06},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2020-01-06T20:18:13.378Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Xie, Chi},\\n journal = {Transportation Letters}\\n}\",\"author_short\":[\"Xie,  C.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"xie-estimationandapplicationofabayesiannetworkmodelfordiscretetravelchoiceanalysis\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Dynamic Bayesian network for crop growth prediction in greenhouses\",\"type\":\"article\",\"year\":\"2020\",\"pages\":\"105167\",\"volume\":\"169\",\"websites\":\"https://linkinghub.elsevier.com/retrieve/pii/S0168169919321131\",\"month\":\"2\",\"id\":\"f33444c1-42a6-3c50-b8e4-26aaa7bd7341\",\"created\":\"2020-01-11T19:54:57.882Z\",\"accessed\":\"2020-01-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2020-01-11T19:54:57.966Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Kocian, A. and Massa, D. and Cannazzaro, S. and Incrocci, L. and Di Lonardo, S. and Milazzo, P. and Chessa, S.\",\"doi\":\"10.1016/j.compag.2019.105167\",\"journal\":\"Computers and Electronics in Agriculture\",\"bibtex\":\"@article{\\n title = {Dynamic Bayesian network for crop growth prediction in greenhouses},\\n type = {article},\\n year = {2020},\\n pages = {105167},\\n volume = {169},\\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0168169919321131},\\n month = {2},\\n id = {f33444c1-42a6-3c50-b8e4-26aaa7bd7341},\\n created = {2020-01-11T19:54:57.882Z},\\n accessed = {2020-01-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2020-01-11T19:54:57.966Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Kocian, A. and Massa, D. and Cannazzaro, S. and Incrocci, L. and Di Lonardo, S. and Milazzo, P. and Chessa, S.},\\n doi = {10.1016/j.compag.2019.105167},\\n journal = {Computers and Electronics in Agriculture}\\n}\",\"author_short\":[\"Kocian,  A.\",\"Massa,  D.\",\"Cannazzaro,  S.\",\"Incrocci,  L.\",\"Di Lonardo,  S.\",\"Milazzo,  P.\",\"Chessa,  S.\"],\"urls\":{\"Website\":\"https://linkinghub.elsevier.com/retrieve/pii/S0168169919321131\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"kocian-massa-cannazzaro-incrocci-dilonardo-milazzo-chessa-dynamicbayesiannetworkforcropgrowthpredictioningreenhouses-2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network\",\"type\":\"article\",\"year\":\"2020\",\"pages\":\"436-443\",\"websites\":\"https://ascopubs.org/doi/10.1200/CCI.19.00136\",\"month\":\"5\",\"id\":\"2b87dcae-451e-35f3-8587-280f7a10ca61\",\"created\":\"2020-05-20T23:04:01.392Z\",\"accessed\":\"2020-05-20\",\"file_attached\":\"true\",\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"379f32c7-d27b-3627-898a-9a4acebc265b\",\"last_modified\":\"2020-05-20T23:04:01.484Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Sieswerda, Melle S. and Bermejo, Inigo and Geleijnse, Gijs and Aarts, Mieke J. and Lemmens, Valery E.P.P. and De Ruysscher, Dirk and Dekker, André L.A.J. and Verbeek, Xander A.A.M\",\"doi\":\"10.1200/CCI.19.00136\",\"journal\":\"JCO Clinical Cancer Informatics\",\"number\":\"4\",\"bibtex\":\"@article{\\n title = {Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network},\\n type = {article},\\n year = {2020},\\n pages = {436-443},\\n websites = {https://ascopubs.org/doi/10.1200/CCI.19.00136},\\n month = {5},\\n id = {2b87dcae-451e-35f3-8587-280f7a10ca61},\\n created = {2020-05-20T23:04:01.392Z},\\n accessed = {2020-05-20},\\n file_attached = {true},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\\n last_modified = {2020-05-20T23:04:01.484Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Sieswerda, Melle S. and Bermejo, Inigo and Geleijnse, Gijs and Aarts, Mieke J. and Lemmens, Valery E.P.P. and De Ruysscher, Dirk and Dekker, André L.A.J. and Verbeek, Xander A.A.M},\\n doi = {10.1200/CCI.19.00136},\\n journal = {JCO Clinical Cancer Informatics},\\n number = {4}\\n}\",\"author_short\":[\"Sieswerda,  M., S.\",\"Bermejo,  I.\",\"Geleijnse,  G.\",\"Aarts,  M., J.\",\"Lemmens,  V., E.\",\"De Ruysscher,  D.\",\"Dekker,  A., L.\",\"Verbeek,  X., A.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/3bce4eca-8ec5-1f05-615c-7c0c8d197cd9/full_text.pdf.pdf\",\"Website\":\"https://ascopubs.org/doi/10.1200/CCI.19.00136\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"sieswerda-bermejo-geleijnse-aarts-lemmens-deruysscher-dekker-verbeek-predictinglungcancersurvivalusingprobabilisticreclassificationoftnmeditionswithabayesiannetwork-2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  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class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"all.bib\" href=\"data:text/bibtex;base64,@article{
 title = {Using hidden multi-state Markov models with multi-parameter volcanic data to provide empirical evidence for alert level decision-support},
 type = {article},
 year = {2006},
 keywords = {Bayesian Belief Network,eruption forecasting,evidence science,hidden Markov model,volcanology},
 pages = {112-124},
 volume = {153},
 websites = {http://www.sciencedirect.com/science/article/pii/S0377027305003872},
 month = {5},
 id = {712032b5-7ec0-3ca8-93e3-be39f0fb306a},
 created = {2015-04-12T18:30:34.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {For the purposes of eruption forecasting and hazard mitigation, a volcanic crisis may be represented as a staged progression of states of unrest, each with its own timescale and likelihood of transition to other states (or to climactic eruption). If the state conditions can be interpreted physically, e.g., in terms of advancing materials failure, this knowledge could be used directly to inform decisions on alert level setting. A multi-state Markov process provides one simple model for defining states and for estimating rates of switching between states. However, for eruptive processes, such states are not directly observable and must be inferred from latent markers, such as seismic activity, gas output, deformation rates, etc., some of which may be contradictory. Interpretations of uncertain data will be liable to error, so a model is needed which can simultaneously estimate both elements: the transition likelihood of a hidden process and the probabilities of state misclassification. We describe the concept and underlying principles of continuous-time hidden Markov models and demonstrate them in a decision-support context with a preliminary working implementation using MULTIMO data. Where multi-parameter streams of raw, processed or conditioned data of different kinds are available, these can be input in near real-time to appropriate hidden multi-state Markov models, the outputs of each providing their own objective analyses of eruptive state in probabilistic terms. These separate, multiple indicators of state can then be input into a Bayesian Belief Network framework for weighing and combining them as different strands of evidence, together with other observations, data, interpretations and expert judgment.},
 bibtype = {article},
 author = {Aspinall, W.P. and Carniel, R. and Jaquet, O. and Woo, G. and Hincks, T.},
 doi = {10.1016/j.jvolgeores.2005.08.010},
 journal = {Journal of Volcanology and Geothermal Research},
 number = {1-2}
}

@article{
 title = {Application of Bayesian in determining productive zones by well log data in oil wells},
 type = {article},
 year = {2012},
 keywords = {Bayesian Network,net pay,petrophysics,productive zone,well test},
 pages = {47-54},
 volume = {94-95},
 websites = {http://www.sciencedirect.com/science/article/pii/S0920410512001702},
 month = {9},
 id = {5e0764ec-d115-302e-b597-ef9875329209},
 created = {2015-04-12T18:30:34.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Exploration specialists conventionally utilize a cut-off-based method to find productive zones inside the oil wells. Using conventional method, pay zones are separated crisply from non-pay zones by applying cut-off values on some petrophysical features. In this paper, a Bayesian technique is developed to find productive zones (net pays), and Bayesian Network is used to select the most appropriate input features for this newly developed method. So, two Bayesian methods were developed: the first one with conventional pay determination inputs (shale percent, porosity and water saturation), the other with two inputs, selected by Bayesian Network (porosity and water saturation). Two developed Bayesian methods are applied on well log dataset of two wells: one well is dedicated for training and testing Bayesian methods, the other for checking generalization ability of the proposed methods. Outputs of two presented methods were compared with the results of conventional cut-off-based method and production test results (i.e. a direct procedure to check validation of proposed methods). Results show that the most prominent advantage of developed Bayesian method is determination of net pays fuzzily with no need to identify cut-offs, in addition to higher precision of classification: nearly 30% improvement in precision of determining net pays of first well (training well), and about 50% improvement in precision of determining productive zones through the generalizing well.},
 bibtype = {article},
 author = {Masoudi, Pedram and Tokhmechi, Behzad and Ansari Jafari, Majid and Zamanzadeh, Seyed Mohammad and Sherkati, Shahram},
 doi = {10.1016/j.petrol.2012.06.028},
 journal = {Journal of Petroleum Science and Engineering}
}

@article{
 title = {Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network.},
 type = {article},
 year = {2009},
 keywords = {Animal Husbandry,Animals,Cattle,Cattle Diseases,Cattle Diseases: epidemiology,Cattle Diseases: prevention & control,Cross-Sectional Studies,Denmark,Denmark: epidemiology,Dermatitis,Dermatitis: epidemiology,Dermatitis: prevention & control,Dermatitis: veterinary,Female,Foot Diseases,Foot Diseases: epidemiology,Foot Diseases: prevention & control,Foot Diseases: veterinary,Hoof and Claw,Lactation,Markov Chains,Monte Carlo Method,Risk Factors,Stochastic Processes},
 pages = {89-98},
 volume = {92},
 websites = {http://www.sciencedirect.com/science/article/pii/S0167587709002323},
 month = {11},
 day = {1},
 id = {79bf4cc8-bb0a-3cb3-bea9-905de32abe19},
 created = {2015-04-12T18:44:12.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Cross sectional data on the prevalence of claw and (inter) digital skin diseases on 4854 Holstein Friesian cows in 50 Danish dairy herds was used in a Bayesian network to create herd specific probability distributions for the presence of lameness causing diseases. Parity and lactation stage are identified as risk factors on cow level, for the prevalence of the three lameness causing diseases digital dermatitits, other infectious diseases and claw horn diseases. Four herd level risk factors have been identified; herd size, the use of footbaths, a grazing strategy and total mixed ration. Besides, the data has been used to estimate the random effect of herd on disease prevalence and to find conditional probabilities of cows being lame, given the presence of the three diseases. By considering the 50 herds representative for the Danish population, the estimates for risk factors, conditional probabilities and random herd effects are used to formulate cow-level probability distributions of disease presence in a specific Danish dairy herd. By step-wise inclusion of information on cow- and herd-level risk factors, lameness prevalence and clinical diagnosis of diseases on cows in the herd, the Bayesian network systematically adjusts the probability distributions for disease presence in the specific herd. Information on population-, herd- and cow-level is combined and the uncertainty in inference on disease probability is quantified.},
 bibtype = {article},
 author = {Ettema, Jehan Frans and Østergaard, Søren and Kristensen, Anders Ringgaard},
 doi = {10.1016/j.prevetmed.2009.08.014},
 journal = {Preventive veterinary medicine},
 number = {1-2}
}

@article{
 title = {Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species},
 type = {article},
 year = {2015},
 keywords = {Bayesian networks,Fisheries management,Kernel density estimation,Pelagic fish,Recruitment forecasting,Supervised classification},
 pages = {35-42},
 volume = {25},
 websites = {http://www.sciencedirect.com/science/article/pii/S1574954114001563},
 month = {1},
 id = {88652dfa-d3a0-3a29-a600-b67b9c00fa5c},
 created = {2015-04-12T18:44:12.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.},
 bibtype = {article},
 author = {Fernandes, Jose A. and Irigoien, Xabier and Lozano, Jose A. and Inza, Iñaki and Goikoetxea, Nerea and Pérez, Aritz},
 doi = {10.1016/j.ecoinf.2014.11.004},
 journal = {Ecological Informatics}
}

@article{
 title = {Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).},
 type = {article},
 year = {2012},
 keywords = {Bayes Theorem,Health Status,Humans,Markov Chains,Parkinson Disease,Quality of Life,Questionnaires},
 pages = {1175-84},
 volume = {45},
 websites = {http://www.sciencedirect.com/science/article/pii/S1532046412001074},
 month = {12},
 id = {7c918dcd-9adb-3846-a93a-08f76992fe8d},
 created = {2015-04-12T18:44:13.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.},
 bibtype = {article},
 author = {Borchani, Hanen and Bielza, Concha and Martı Nez-Martı N, Pablo and Larrañaga, Pedro},
 doi = {10.1016/j.jbi.2012.07.010},
 journal = {Journal of biomedical informatics},
 number = {6}
}

@article{
 title = {A Bayesian network model for predicting pregnancy after in vitro fertilization.},
 type = {article},
 year = {2013},
 keywords = {Adult,Algorithms,Area Under Curve,Bayes Theorem,Computer Simulation,Embryo Transfer,Embryo Transfer: statistics & numerical data,Embryo, Mammalian,Female,Fertilization in Vitro,Fertilization in Vitro: statistics & numerical dat,Humans,Pregnancy,Pregnancy: statistics & numerical data},
 pages = {1783-92},
 volume = {43},
 websites = {http://www.sciencedirect.com/science/article/pii/S0010482513002187},
 month = {11},
 id = {ea7467fd-0819-3352-ac67-6489e0f12820},
 created = {2015-04-12T18:44:13.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {We present a Bayesian network model for predicting the outcome of in vitro fertilization (IVF). The problem is characterized by a particular missingness process; we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. We present results with generated data and the analysis of a real data set. Moreover, we assess by means of a simulation study the effectiveness of the model in supporting the selection of the embryos to be transferred.},
 bibtype = {article},
 author = {Corani, G and Magli, C and Giusti, A and Gianaroli, L and Gambardella, L M},
 doi = {10.1016/j.compbiomed.2013.07.035},
 journal = {Computers in biology and medicine},
 number = {11}
}

@article{
 title = {Diagnosis of breast cancer using Bayesian networks: a case study.},
 type = {article},
 year = {2007},
 keywords = {Algorithms,Bayes Theorem,Biopsy, Fine-Needle,Breast Neoplasms,Breast Neoplasms: diagnosis,Cytodiagnosis,Cytodiagnosis: statistics & numerical data,Databases, Factual,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: statistics & numeric,Female,Humans,Observer Variation},
 pages = {1553-64},
 volume = {37},
 websites = {http://www.sciencedirect.com/science/article/pii/S0010482507000340},
 month = {11},
 id = {c166c75a-987f-386e-9dbf-98e6a0dc37eb},
 created = {2015-04-12T18:44:13.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {We evaluate the effectiveness of seven Bayesian network classifiers as potential tools for the diagnosis of breast cancer using two real-world databases containing fine-needle aspiration of the breast lesion cases collected by a single observer and multiple observers, respectively. The results show a certain ingredient of subjectivity implicitly contained in these data: we get an average accuracy of 93.04% for the former and 83.31% for the latter. These findings suggest that observers see different things when looking at the samples in the microscope; a situation that significantly diminishes the performance of these classifiers in diagnosing such a disease.},
 bibtype = {article},
 author = {Cruz-Ramírez, Nicandro and Acosta-Mesa, Héctor Gabriel and Carrillo-Calvet, Humberto and Nava-Fernández, Luis Alonso and Barrientos-Martínez, Rocío Erandi},
 doi = {10.1016/j.compbiomed.2007.02.003},
 journal = {Computers in biology and medicine},
 number = {11}
}

@article{
 title = {A data mining approach to predictive vegetation mapping using probabilistic graphical models},
 type = {article},
 year = {2011},
 keywords = {Bayesian network,Data mining,Expectation-maximization,Graphical model,Predictive vegetation mapping},
 pages = {111-124},
 volume = {6},
 websites = {http://www.sciencedirect.com/science/article/pii/S1574954111000045},
 month = {3},
 id = {4b6617c5-cc10-33bd-a91f-6af94ae4a28f},
 created = {2015-04-12T18:44:13.000Z},
 accessed = {2015-02-16},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper develops a novel method to model and predict the spatial distribution of vegetation types in Swaziland using physiographic and bioclimatic variables. The method uses a data mining approach implemented within a probabilistic graphical model to match two observed hierarchical levels of vegetation. The classification uses Bayesian networks (BN) and the parameterization is based on the expectation-maximization (EM) algorithm. The model is tested on a random sample of mapped vegetation types in Swaziland and allowed for the identification of the key environmental variables that are most important for capturing the vegetation spatial distribution. We show that while elevation and geology are the most important variables explaining the spatial distribution patterns of vegetation for both models, the influence of the climatic and other variables on the vegetation at the two levels differ. The overall distribution of the predicted vegetation classes was very similar to their distribution on the observed vegetation map. Overall the error rate was found to be 9.35% for a model of 16 vegetation classes and 4.9% for the one with 5 classes, indicating the excellent classification accuracy of the approach despite the complex landscape of the study area. Possible sources of error and some limitations are discussed and conclusions are drawn including suggestions for further investigation.},
 bibtype = {article},
 author = {Dlamini, Wisdom M.},
 doi = {10.1016/j.ecoinf.2010.12.005},
 journal = {Ecological Informatics},
 number = {2}
}

@article{
 title = {A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features},
 type = {article},
 year = {2014},
 keywords = {Bayesian network,Development,Habitat,Piping plover,Sea-level rise,Shorebird},
 pages = {38-50},
 volume = {276},
 websites = {http://www.sciencedirect.com/science/article/pii/S0304380014000398},
 month = {3},
 id = {086f8466-6471-3150-a892-146a13a8d70a},
 created = {2015-04-12T18:51:29.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modeling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model's dataset. We found that model predictions were more successful when the ranges of physical conditions included in model development were varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modeling impacts of sea-level rise or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.},
 bibtype = {article},
 author = {Gieder, Katherina D. and Karpanty, Sarah M. and Fraser, James D. and Catlin, Daniel H. and Gutierrez, Benjamin T. and Plant, Nathaniel G. and Turecek, Aaron M. and Robert Thieler, E.},
 doi = {10.1016/j.ecolmodel.2014.01.005},
 journal = {Ecological Modelling}
}

@article{
 title = {Learning and modeling biosignatures from tissue images.},
 type = {article},
 year = {2007},
 keywords = {Algorithms,Animals,Artificial Intelligence,Bayes Theorem,Computer Simulation,Diagnosis, Computer-Assisted,Humans,Image Interpretation, Computer-Assisted,Infection,Infection: classification,Infection: diagnosis,Lung,Lung Diseases,Lung Diseases: diagnosis,Lung: anatomy & histology,Mice},
 pages = {1539-52},
 volume = {37},
 websites = {http://www.sciencedirect.com/science/article/pii/S0010482507000339},
 month = {11},
 id = {57bfce04-027f-341a-a291-194cee674ec5},
 created = {2015-04-12T18:51:29.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Ideally biosignatures can be detected at the early infection phase and used both for developing diagnostic patterns and for prognostic triage. Such biosignatures are important for vaccine validation and to provide risk stratification to a population such as for the identification of individuals who are exposed to biological or chemical agents and who are at high risk for developing an infection. The research goal is to detect broad based biosignature models and is initially focused on developing effective computer-augmented pathology tied to animal models developed at the University of New Mexico (UNM). Using lung tissue from infected and nai ve mice, feature extraction from images of the tissue under a specialized microscope, and Bayesian networks to analyze the data sets of features, we were able to differentiate normal from diseased samples and viral from bacterial samples in mid to late stages of infection. This effort has shown the potential effectiveness of computer-augmented pathology in this application. The extended research intends to couple analysis of serum, microarray analysis of organs, proteomic data and the pathology. The rational for the current invasive procedure on animal models is to facilitate the development of data analysis and machine learning techniques that can eventually be generalized to the task of discovering non-invasive and early stage biosignatures for human models.},
 bibtype = {article},
 author = {Gilfeather, Frank and Hamine, Vikas and Helman, Paul and Hutt, Julie and Loring, Terry and Lyons, C Rick and Veroff, Robert},
 doi = {10.1016/j.compbiomed.2007.02.005},
 journal = {Computers in biology and medicine},
 number = {11}
}

@article{
 title = {A meta-assessment for elasmobranchs based on dietary data and Bayesian networks},
 type = {article},
 year = {2002},
 keywords = {Bayesian networks,Elasmobranchs,Fisheries management,Food webs,Threatened species},
 pages = {197-211},
 volume = {1},
 websites = {http://www.sciencedirect.com/science/article/pii/S1470160X02000055},
 month = {3},
 id = {043feed1-8366-3a45-a06f-512aa682a2de},
 created = {2015-04-12T18:51:29.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {We developed a new approach, meta-assessment, as a tool for identifying declining (and potentially threatened) fish stocks in situations where a lack of data precludes traditional stock assessments. Meta-assessments are models enhanced by the incorporation of other stock assessment results. We used this idea to estimate historic biomass trends for demersal elasmobranchs of the Irish Sea. Bayesian networks, constructed from published dietary data and resembling food webs, allowed us to incorporate into our estimates the results from virtual population analysis (VPA) for Irish Sea cod, sole, plaice and whiting. To assess accuracy, we used cross-validation, estimating historic biomass trends in each individual VPA species from trends in the other three plus trends in fishing effort. We compared predicted annual trends to those derived from VPA and found 66% accuracy. We also compared biomass trends estimated from annual trawl surveys to corresponding network predictions, recovering survey trends correctly 61% of the time for elasmobranchs, 78% of the time for gurnards (Triglidae) and 89% for bib and pout (Trisopterus spp.). Results suggest that of the 11 elasmobranchs examined, the angel shark (Squatina squatina) increased in biomass least frequently from 1987 to 1997, a view consistent with survey results. Our approach also suggested a marked decline in common skate (Dipturus batis) over the period 1965–1978, during which time the skate was extirpated from the Irish Sea. We conclude that meta-assessment can serve as an exploratory method for identifying potentially threatened stocks, where even landings data are unavailable.},
 bibtype = {article},
 author = {Hammond, T.R and Ellis, J.R},
 doi = {10.1016/S1470-160X(02)00005-5},
 journal = {Ecological Indicators},
 number = {3}
}

@article{
 title = {Quantifying the determinants of outbreak detection performance through simulation and machine learning.},
 type = {article},
 year = {2015},
 keywords = {Bayesian networks,Disease outbreak detection,Outbreak simulation,Predicting performance,Public health informatics,Surveillance},
 pages = {180-7},
 volume = {53},
 websites = {http://www.sciencedirect.com/science/article/pii/S1532046414002299},
 month = {2},
 id = {d7500789-9ac9-3544-924e-f336004042ac},
 created = {2015-04-12T18:51:29.000Z},
 accessed = {2015-03-17},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.

MATERIALS AND METHODS: We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation.

RESULTS: The model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns.

CONCLUSION: We developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems.},
 bibtype = {article},
 author = {Jafarpour, Nastaran and Izadi, Masoumeh and Precup, Doina and Buckeridge, David L},
 doi = {10.1016/j.jbi.2014.10.009},
 journal = {Journal of biomedical informatics}
}

@article{
 title = {Machine learning applications in cancer prognosis and prediction},
 type = {article},
 year = {2015},
 keywords = {ANN,AUC,Area Under Curve,Artificial Neural Network,BCRSVM,BN,Bayesian Network,Breast Cancer Support Vector Machine,CFS,Cancer recurrence,Cancer survival,Cancer susceptibility,Correlation based Feature Selection,DT,Decision Tree,ES,Early Stopping algorithm,GEO,Gene Expression Omnibus,HTT,High-throughput Technologies,LCS,Learning Classifying Systems,ML,Machine Learning,Machine learning,NCI caArray,NSCLC,National Cancer Institute Array Data Management Sy,Non-small Cell Lung Cancer,OSCC,Oral Squamous Cell Carcinoma,PPI,Predictive models,Protein–Protein Interaction,ROC,Receiver Operating Characteristic,SEER,SSL,SVM,Semi-supervised Learning,Support Vector Machine,Surveillance, Epidemiology and End results Databas,TCGA,The Cancer Genome Atlas Research Network},
 pages = {8-17},
 volume = {13},
 websites = {http://www.sciencedirect.com/science/article/pii/S2001037014000464},
 month = {12},
 id = {6549cc53-5dde-30fc-94cf-a29fdd5890b5},
 created = {2015-04-12T18:59:39.000Z},
 accessed = {2015-01-01},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.},
 bibtype = {article},
 author = {Kourou, Konstantina and Exarchos, Themis P. and Exarchos, Konstantinos P. and Karamouzis, Michalis V. and Fotiadis, Dimitrios I.},
 doi = {10.1016/j.csbj.2014.11.005},
 journal = {Computational and Structural Biotechnology Journal}
}

@article{
 title = {Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data},
 type = {article},
 year = {2011},
 keywords = {Knowledge Discovery},
 pages = {109-115},
 volume = {100},
 websites = {http://www.sciencedirect.com/science/article/pii/S0167587711000341},
 month = {6},
 id = {d7020f3c-f6cc-3407-b484-b635b8d5047b},
 created = {2015-04-12T19:14:40.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Analysing animal health data can be a complex task as the health status of individuals or groups of animals, might depend on many inter-related variables. The objective is to differentiate variables that are directly associated with health status and therefore promising targets for intervention, from variables that are indirectly associated with health status and can therefore at best only affect this indirectly through association with other variables. Bayesian network (BN) modelling is a machine learning technique for empirically identifying associations in complex and high dimensional data, so-called “structure discovery”. An introduction to structure discovery using BN modelling is presented, comprising the key assumptions required by the methodology, along with a discussion of advantages and limitations. To demonstrate the various steps required to apply BN structure discovery to animal health data, illustrative analyses of data collected during a previously published study concerned with exposure to bovine viral diarrhoea virus in beef cow-calf herds in Scotland are presented.},
 bibtype = {article},
 author = {Lewis, F.I. and Brülisauer, F. and Gunn, G.J.},
 doi = {10.1016/j.prevetmed.2011.02.003},
 journal = {Preventive Veterinary Medicine},
 number = {2}
}

@article{
 title = {Classification of fluorescence in situ hybridization images using belief networks},
 type = {article},
 year = {2004},
 keywords = {Belief networks,Fluorescence in situ hybridization (FISH),Image classification,K2 algorithm,Naive Bayesian classifier},
 pages = {1777-1785},
 volume = {25},
 websites = {http://www.sciencedirect.com/science/article/pii/S0167865504001710},
 month = {12},
 id = {3f97b562-8d2e-32ad-8dbe-3bef87896c76},
 created = {2015-04-12T19:47:10.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning.},
 bibtype = {article},
 author = {Malka, Roy and Lerner, Boaz},
 doi = {10.1016/j.patrec.2004.07.010},
 journal = {Pattern Recognition Letters},
 number = {16}
}

@article{
 title = {Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach},
 type = {article},
 year = {2009},
 keywords = {Bayesian belief network,Diadema antillarum,Dictyota spp.,Grazing pressure,Macroalgal dynamics,Nutrients,Scaridae},
 pages = {1305-1314},
 volume = {220},
 websites = {http://www.sciencedirect.com/science/article/pii/S0304380009001525},
 month = {5},
 id = {7499dc3b-15d2-3b2c-8cc1-7623649b11d9},
 created = {2015-04-12T20:17:32.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Macroalgae are a major benthic component of coral reefs and their dynamics influence the resilience of coral reefs to disturbance. However, the relative importance of physical and ecological processes in driving macroalgal dynamics is poorly understood. Here we develop a Bayesian belief network (BBN) model to integrate many of these processes and predict the growth of coral reef macroalgae. Bayesian belief networks use probabilistic relationships rather than deterministic rules to quantify the cause and effect assumptions. The model was developed using both new empirical data and quantified relationships elicited from previous studies. We demonstrate the efficacy of the BBN to predict the dynamics of a common Caribbean macroalgal genus Dictyota. Predictions of the model have an average accuracy of 55% (implying that 55% of the predicted categories of Dictyota cover were assigned to the correct class). Sensitivity analysis suggested that macroalgal dynamics were primarily driven by top–down processes of grazing rather than bottom–up nutrification. BBNs provide a useful framework for modelling complex systems, identifying gaps in our scientific understanding and communicating the complexities of the associated uncertainties in an explicit manner to stakeholders. We anticipate that accuracies will improve as new data are added to the model.},
 bibtype = {article},
 author = {Renken, Henk and Mumby, Peter J.},
 doi = {10.1016/j.ecolmodel.2009.02.022},
 journal = {Ecological Modelling},
 number = {9-10}
}

@article{
 title = {Dynamic Bayesian Networks to predict sequences of organ failures in patients admitted to ICU.},
 type = {article},
 year = {2014},
 keywords = {Aged,Algorithms,Bayes Theorem,Critical Illness,Decision Support Systems, Clinical,Female,Hospital Mortality,Humans,Intensive Care,Intensive Care Units,Intensive Care: methods,Length of Stay,Male,Middle Aged,Multiple Organ Failure,Multiple Organ Failure: physiopathology,Multiple Organ Failure: therapy,Probability,Prognosis,Software,Time Factors},
 pages = {106-13},
 volume = {48},
 websites = {http://www.sciencedirect.com/science/article/pii/S1532046413001998},
 month = {4},
 id = {009707e8-f54a-399f-95c0-b1de38f6d52a},
 created = {2015-04-12T20:17:32.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Multi Organ Dysfunction Syndrome (MODS) represents a continuum of physiologic derangements and is the major cause of death in the Intensive Care Unit (ICU). Scoring systems for organ failure have become an integral part of critical care practice and play an important role in ICU-based research by tracking disease progression and facilitating patient stratification based on evaluation of illness severity during ICU stay. In this study a Dynamic Bayesian Network (DBN) was applied to model SOFA severity score changes in 79 adult critically ill patients consecutively admitted to the general ICU of the Sant'Andrea University hospital (Rome, Italy) from September 2010 to March 2011, with the aim to identify the most probable sequences of organs failures in the first week after the ICU admission. Approximately 56% of patients were admitted into the ICU with lung failure and about 27% of patients with heart failure. Results suggest that, given the first organ failure at the ICU admission, a sequence of organ failures can be predicted with a certain degree of probability. Sequences involving heart, lung, hematologic system and liver turned out to be the more likely to occur, with slightly different probabilities depending on the day of the week they occur. DBNs could be successfully applied for modeling temporal systems in critical care domain. Capability to predict sequences of likely organ failures makes DBNs a promising prognostic tool, intended to help physicians in undertaking therapeutic decisions in a patient-tailored approach.},
 bibtype = {article},
 author = {Sandri, Micol and Berchialla, Paola and Baldi, Ileana and Gregori, Dario and De Blasi, Roberto Alberto},
 doi = {10.1016/j.jbi.2013.12.008},
 journal = {Journal of biomedical informatics}
}

@article{
 title = {A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment.},
 type = {article},
 year = {2014},
 keywords = {Alzheimer Disease,Alzheimer Disease: classification,Alzheimer Disease: diagnosis,Alzheimer Disease: physiopathology,Bayes Theorem,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Mental Disorders,Mental Disorders: classification,Mental Disorders: diagnosis,Mental Disorders: physiopathology,Models, Biological},
 pages = {140-58},
 volume = {51},
 websites = {http://www.sciencedirect.com/science/article/pii/S0010482514000961},
 month = {8},
 id = {098541ed-8785-3bdd-9a21-5db89d4f8b85},
 created = {2015-04-12T20:17:33.000Z},
 accessed = {2015-04-08},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.},
 bibtype = {article},
 author = {Seixas, Flávio Luiz and Zadrozny, Bianca and Laks, Jerson and Conci, Aura and Muchaluat Saade, Débora Christina},
 doi = {10.1016/j.compbiomed.2014.04.010},
 journal = {Computers in biology and medicine}
}

@article{
 title = {Dynamic Bayesian networks as prognostic models for clinical patient management.},
 type = {article},
 year = {2008},
 keywords = {Algorithms,Bayes Theorem,Carcinoid Tumor,Carcinoid Tumor: diagnosis,Carcinoid Tumor: mortality,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Neural Networks (Computer),Prognosis,Risk Assessment,Risk Assessment: methods,Risk Factors,Survival Analysis,Survival Rate},
 pages = {515-29},
 volume = {41},
 websites = {http://www.sciencedirect.com/science/article/pii/S1532046408000154},
 month = {8},
 id = {ce5b78cb-e69e-3915-8050-fc5b21a5b41c},
 created = {2015-04-12T20:17:33.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Prognostic models in medicine are usually been built using simple decision rules, proportional hazards models, or Markov models. Dynamic Bayesian networks (DBNs) offer an approach that allows for the incorporation of the causal and temporal nature of medical domain knowledge as elicited from domain experts, thereby allowing for detailed prognostic predictions. The aim of this paper is to describe the considerations that must be taken into account when constructing a DBN for complex medical domains and to demonstrate their usefulness in practice. To this end, we focus on the construction of a DBN for prognosis of carcinoid patients, compare performance with that of a proportional hazards model, and describe predictions for three individual patients. We show that the DBN can make detailed predictions, about not only patient survival, but also other variables of interest, such as disease progression, the effect of treatment, and the development of complications. Strengths and limitations of our approach are discussed and compared with those offered by traditional methods.},
 bibtype = {article},
 author = {van Gerven, Marcel A J and Taal, Babs G and Lucas, Peter J F},
 doi = {10.1016/j.jbi.2008.01.006},
 journal = {Journal of biomedical informatics},
 number = {4}
}

@article{
 title = {Learning Bayesian networks for clinical time series analysis.},
 type = {article},
 year = {2014},
 keywords = {Aged,Algorithms,Area Under Curve,Artificial Intelligence,Bayes Theorem,Computer Simulation,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Female,Humans,Lung,Lung: physiology,Male,Middle Aged,Monitoring, Ambulatory,Monitoring, Ambulatory: methods,Probability,Pulmonary Disease, Chronic Obstructive,Pulmonary Disease, Chronic Obstructive: therapy,Signal Processing, Computer-Assisted,Time Factors},
 pages = {94-105},
 volume = {48},
 websites = {http://www.sciencedirect.com/science/article/pii/S1532046413001986},
 month = {4},
 id = {5a113d64-7b66-3d77-88c5-32fbe2e25e03},
 created = {2015-04-12T20:17:34.000Z},
 accessed = {2015-04-07},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {INTRODUCTION: Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status.

METHODS: Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set.

RESULTS: The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.},
 bibtype = {article},
 author = {van der Heijden, Maarten and Velikova, Marina and Lucas, Peter J F},
 doi = {10.1016/j.jbi.2013.12.007},
 journal = {Journal of biomedical informatics}
}

@article{
 title = {Prognostic Bayesian networks I: rationale, learning procedure, and clinical use.},
 type = {article},
 year = {2007},
 keywords = {Algorithms,Artificial Intelligence,Bayes Theorem,Computer Simulation,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Models, Biological,Models, Statistical,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Prognosis},
 pages = {609-18},
 volume = {40},
 websites = {http://www.sciencedirect.com/science/article/pii/S1532046407000615},
 month = {12},
 id = {3a2e1100-6966-3420-a67d-82df563416d6},
 created = {2015-04-12T20:17:34.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Prognostic models are tools to predict the future outcome of disease and disease treatment, one of the fundamental tasks in clinical medicine. This article presents the prognostic Bayesian network (PBN) as a new type of prognostic model that builds on the Bayesian network methodology, and implements a dynamic, process-oriented view on prognosis. A PBN describes the mutual relationships between variables that come into play during subsequent stages of a care process and a clinical outcome. A dedicated procedure for inducing these networks from clinical data is presented. In this procedure, the network is composed of a collection of local supervised learning models that are recursively learned from the data. The procedure optimizes performance of the network's primary task, outcome prediction, and handles the fact that patients may drop out of the process in earlier stages. Furthermore, the article describes how PBNs can be applied to solve a number of information problems that are related to medical prognosis.},
 bibtype = {article},
 author = {Verduijn, Marion and Peek, Niels and Rosseel, Peter M J and de Jonge, Evert and de Mol, Bas A J M},
 doi = {10.1016/j.jbi.2007.07.003},
 journal = {Journal of biomedical informatics},
 number = {6}
}

@article{
 title = {Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: a case study of Taiwan.},
 type = {article},
 year = {2014},
 keywords = {Aged,Algorithms,Bayes Theorem,Brain Neoplasms,Brain Neoplasms: epidemiology,Brain Neoplasms: secondary,Computational Biology,Computational Biology: methods,Female,Humans,Lung Neoplasms,Lung Neoplasms: epidemiology,Lung Neoplasms: pathology,Male,Middle Aged,Models, Statistical,Sensitivity and Specificity,Taiwan,Taiwan: epidemiology},
 pages = {147-60},
 volume = {47},
 websites = {http://www.sciencedirect.com/science/article/pii/S001048251400033X},
 month = {4},
 id = {c853e323-681e-3ae4-80c6-3b543722b7f4},
 created = {2015-04-12T20:17:34.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The Bayesian network (BN) is a promising method for modeling cancer metastasis under uncertainty. BN is graphically represented using bioinformatics variables and can be used to support an informative medical decision/observation by using probabilistic reasoning. In this study, we propose such a BN to describe and predict the occurrence of brain metastasis from lung cancer. A nationwide database containing more than 50,000 cases of cancer patients from 1996 to 2010 in Taiwan was used in this study. The BN topology for studying brain metastasis from lung cancer was rigorously examined by domain experts/doctors. We used three statistical measures, namely, the accuracy, sensitivity, and specificity, to evaluate the performances of the proposed BN model and to compare it with three competitive approaches, namely, naive Bayes (NB), logistic regression (LR) and support vector machine (SVM). Experimental results show that no significant differences are observed in accuracy or specificity among the four models, while the proposed BN outperforms the others in terms of sampled average sensitivity. Moreover the proposed BN has advantages compared with the other approaches in interpreting how brain metastasis develops from lung cancer. It is shown to be easily understood by physicians, to be efficient in modeling non-linear situations, capable of solving stochastic medical problems, and handling situations wherein information are missing in the context of the occurrence of brain metastasis from lung cancer.},
 bibtype = {article},
 author = {Wang, Kung-Jeng and Makond, Bunjira and Wang, Kung-Min},
 doi = {10.1016/j.compbiomed.2014.02.002},
 journal = {Computers in biology and medicine}
}

@article{
 title = {Not just data: a method for improving prediction with knowledge.},
 type = {article},
 year = {2014},
 keywords = {Algorithms,Bayes Theorem,Blood Coagulation,Blood Coagulation Disorders,Blood Coagulation Disorders: therapy,Cluster Analysis,Decision Making,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Emergency Medical Services,Emergency Medical Services: organization & adminis,Humans,Medical Errors,Medical Errors: prevention & control,Medical Informatics,Medical Informatics: methods,Medical Informatics: trends,Risk Assessment,Sensitivity and Specificity},
 pages = {28-37},
 volume = {48},
 websites = {http://www.sciencedirect.com/science/article/pii/S1532046413001640},
 month = {4},
 id = {1d43401a-3c97-343a-b1b0-d9e0f0ab9781},
 created = {2015-04-12T20:17:35.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.},
 bibtype = {article},
 author = {Yet, Barbaros and Perkins, Zane and Fenton, Norman and Tai, Nigel and Marsh, William},
 doi = {10.1016/j.jbi.2013.10.012},
 journal = {Journal of biomedical informatics}
}

@article{
 title = {Using a Bayesian belief network to aid differential diagnosis of tropical bovine diseases},
 type = {article},
 year = {2000},
 keywords = {Africa,Bayesian belief network,Cattle disease,Differential diagnosis,Expert system},
 pages = {141-156},
 volume = {47},
 websites = {http://www.sciencedirect.com/science/article/pii/S0167587700001720},
 month = {11},
 id = {c6fb9f5d-9401-3d6e-a6ec-1fc41a7a7912},
 created = {2015-04-12T20:17:35.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The examination of presenting signs has always played an important role in the diagnosis of diseases in animal populations. In the case of diseases of tropical cattle, such expertise is often scarce and confined to those experts with many years of experience. To capture, conserve and disseminate such valuable expert knowledge remains a key challenge to the application of knowledge-based systems in veterinary medicine. In this communication, we explore the use of a Bayesian belief network to quantify expert opinion with a view to estimating the likelihood of various diseases in the presence and absence of certain signs. Information was elicited from a panel of 44 experienced veterinarians to provide the response matrix of 27 signs associated with 20 commonly occurring diseases in sub-Saharan cattle. Using this prior information, estimates of the probability of certain signs occurring with each disease were calculated from which the Bayesian belief network was able to propagate the posterior probability of each of the diseases based on the observed signs. The method as an aid in making diagnosis is discussed. It is recognised that such an approach is but one strand in the process of arriving at a diagnosis. For ease of use and accessibility, the approach has been converted into the software program CaDDiS (Cattle Disease Diagnosis System) which is available for consultation on the World Wide Web.},
 bibtype = {article},
 author = {McKendrick, I.J and Gettinby, G and Gu, Y and Reid, S.W.J and Revie, C.W},
 doi = {10.1016/S0167-5877(00)00172-0},
 journal = {Preventive Veterinary Medicine},
 number = {3}
}

@article{
 title = {Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index},
 type = {article},
 year = {2013},
 keywords = {Bayesian network,Early stage entrepreneurial activity Index,Forecasting,GEM},
 pages = {4003-4009},
 volume = {40},
 websites = {http://www.sciencedirect.com/science/article/pii/S0957417413000122},
 month = {8},
 id = {4f4aa7b1-b0f6-3c9a-a260-cb8d1af6a8a8},
 created = {2015-04-16T00:39:20.000Z},
 accessed = {2015-02-14},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Entrepreneurship plays a critical role for the development and well-being of society. Illustration of its dynamic relationship with entrepreneurial attitudes and aspirations can provide a guideline for the cause of such activities. However, a time-lagged causal relationship among these concepts has not yet been established. In this study, we examine a dynamic relationship among early stage entrepreneurial attitudes, activities, and aspirations using Bayesian network (BN) analysis. In addition, we propose an early stage entrepreneurial activity index that can predict the percentage of both nascent entrepreneur and new business owner using the variables related to entrepreneurial attitudes of the previous year. This index, in turn, can be used to predict various aspects of entrepreneurial aspiration of the following year. The proposed index turns out to have very high prediction accuracy and is expected to provide effective policies to boost future entrepreneurial activity and aspiration.},
 bibtype = {article},
 author = {Sohn, So Young and Lee, Ann Sung},
 doi = {10.1016/j.eswa.2013.01.009},
 journal = {Expert Systems with Applications},
 number = {10}
}

@article{
 title = {A driver fatigue recognition model based on information fusion and dynamic Bayesian network},
 type = {article},
 year = {2010},
 keywords = {Contextual features,Driver fatigue recognition,Dynamic Bayesian network,Information fusion,Physiological features},
 pages = {1942-1954},
 volume = {180},
 websites = {http://www.sciencedirect.com/science/article/pii/S0020025510000253},
 month = {5},
 id = {af17e830-7378-3917-9df9-516b00560306},
 created = {2015-04-17T15:30:41.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver.},
 bibtype = {article},
 author = {Yang, Guosheng and Lin, Yingzi and Bhattacharya, Prabir},
 doi = {10.1016/j.ins.2010.01.011},
 journal = {Information Sciences},
 number = {10}
}

@article{
 title = {A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features},
 type = {article},
 year = {2014},
 keywords = {Bayesian network,Development,Habitat,Piping plover,Sea-level rise,Shorebird},
 pages = {38-50},
 volume = {276},
 websites = {https://darchive.mblwhoilibrary.org/handle/1912/7233},
 month = {3},
 publisher = {Elsevier},
 day = {31},
 id = {18bdf9c6-a58f-3f8a-8215-c2758447ea1d},
 created = {2015-05-07T19:12:09.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2017-03-14T14:38:49.606Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 language = {en_US},
 private_publication = {false},
 abstract = {© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Modelling 276 (2014): 38–50, doi:10.1016/j.ecolmodel.2014.01.005.},
 bibtype = {article},
 author = {Gieder, Katherina D. and Karpanty, Sarah M. and Fraser, James D. and Catlin, Daniel H. and Gutierrez, Benjamin T. and Plant, Nathaniel G. and Turecek, Aaron M. and Robert Thieler, E.},
 doi = {10.1016/j.ecolmodel.2014.01.005},
 journal = {Ecological Modelling}
}

@article{
 title = {Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis},
 type = {article},
 year = {2018},
 websites = {http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf},
 id = {5b5c1742-115d-3d0d-9205-dae3a9e85e95},
 created = {2018-03-31T21:53:37.316Z},
 accessed = {2018-03-31},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2018-03-31T23:54:10.489Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {— The aim of the investigation was to analyse patient clinical data of Bronchiectasis patients in a Singaporean cohort in order to identify any unknown correlations between clinical parameters such as age, sex, presence of certain organisms in body etc. Both supervised learning and unsupervised machine learning computer algorithms were applied to the data set. A strong inverse correlation was found between the A.fumigatus burden level and P.aeruginosa status in the pulmonary track of patients. Differences in cytokine expressions in the body, which were found to be statistically significant, were also observed, and this could be used as a more accurate biomarker to flag Aspergillus as compared to the poorly accurate culture test which is currently used. Future research needs to be conducted to further investigate the relationship between A.fumigatus and P.aeruginosa and the viability of using the identified cytokines as biomarkers.},
 bibtype = {article},
 author = {Venkatesh, Tejas}
}

@article{
 title = {Reverse deduction of vehicle group situation based on dynamic Bayesian network},
 type = {article},
 year = {2018},
 keywords = {Poisson's distribution,Vehicle group situation,dynamic Bayesian network,privacy protection,reverse deduction},
 pages = {1-15},
 volume = {10},
 websites = {http://journals.sagepub.com/doi/pdf/10.1177/1687814017747708},
 id = {fd96fbef-7906-3746-a462-6a5cb1829191},
 created = {2018-03-31T23:32:39.494Z},
 accessed = {2018-03-31},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2018-03-31T23:54:10.482Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Vehicle group is the basic unit of microscopic traffic flow, and also a concept that often involved in the research of active vehicle security. It is of great significance to identify vehicle group situation accurately for the research of traffic flow the-ory and the intelligent vehicle driving system. Three-lane condition was taken as an example and the privacy protection of driver (only the data of travel time were used) was a premise in this article. Poisson's distribution was used to identify vehicle group situation which was constituted by target vehicle and its neighboring vehicles when the target vehicle arrived at the end of study area. And the dynamic Bayesian network was used to build the reverse deduction model of vehicle group situation. The model was verified through actual and virtual driving experiments. Verification results showed that the model established in this article was reasonable and feasible.},
 bibtype = {article},
 author = {Wang, Xiaoyuan and Wang, Jianqiang and Liu, Yaqi and Liu, Zhenxue and Wang, Jingheng},
 doi = {10.1177/1687814017747708},
 journal = {Special Issue Article Advances in Mechanical Engineering},
 number = {3}
}

@article{
 title = {Analysis and prediction of student placement for improving the education standards},
 type = {article},
 year = {2018},
 keywords = {academics,association rules,decision tree,j48 algorithm,placement,prediction,universities},
 pages = {303-306},
 volume = {7},
 id = {75742eca-76db-39d0-b3ba-ea4502c587e8},
 created = {2018-03-31T23:54:09.395Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2018-03-31T23:54:09.395Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Devi, S Anjali and Priya, M Vishnu and Akhila, P and Vasundhara, N}
}

@article{
 title = {Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials},
 type = {article},
 year = {2018},
 keywords = {Mechanical properties,Metals and alloys},
 pages = {35},
 volume = {4},
 websites = {http://www.nature.com/articles/s41524-018-0094-7},
 month = {12},
 publisher = {Nature Publishing Group},
 day = {16},
 id = {dc81e604-655d-3c91-a4b7-d70394916669},
 created = {2018-07-23T01:03:03.039Z},
 accessed = {2018-07-22},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2018-07-23T01:03:03.039Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature. Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics.},
 bibtype = {article},
 author = {Rovinelli, Andrea and Sangid, Michael D. and Proudhon, Henry and Ludwig, Wolfgang},
 doi = {10.1038/s41524-018-0094-7},
 journal = {npj Computational Materials},
 number = {1}
}

@article{
 title = {Prediction of drivers and pedestrians' behaviors at signalized mid-block Danish offset crosswalks using Bayesian networks},
 type = {article},
 year = {2019},
 pages = {75-83},
 volume = {69},
 websites = {https://www.sciencedirect.com/science/article/pii/S0022437518306595?dgcid=author#!},
 month = {6},
 publisher = {Pergamon},
 day = {1},
 id = {0b551964-6737-3715-9f19-89171b033552},
 created = {2019-03-23T04:04:22.832Z},
 accessed = {2019-03-23},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2019-03-23T04:04:22.918Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Introduction: This study presents the prediction of driver yielding compliance and pedestrian tendencies to press pushbuttons at signalized mid-block Danish offset crosswalks. Method: It applies Bayesian Networks (BNs) analysis, which is basically a graphical non-functional form model, on observational survey data collected from five signalized crosswalks in Las Vegas, Nevada. The BNs structures were found in the data by applying several score functions. By considering prediction accuracy and the Area under the Receiver Operating Characteristic (ROC) curves, the BN found that using the Bayesian Information Criterion (BIC) score resulted in the best network structure, compared to the ones found using K2 and the Akaike Information Criterion (AIC). The BIC score-based structure was then used for parameter learning and probabilistic inference. Results: Results show that, when considering an individual scenario, the highest predicted yielding compliance (81%) is attained when pedestrians arrive at the crosswalk while the flashes are active, whereas the lowest predicted yielding compliance (23.4%) is observed when the pedestrians cross between the yield line and advanced pedestrian crosswalk sign. On the other hand, crossing within marked stripes, approaching the crosswalk from the near side of the pushbutton pole, inactive flashing lights, and being the first to arrive at the crosswalk result in relatively high-predicted probabilities of pedestrians pressing pushbutton. Furthermore, with a combination of scenarios, the maximum achievable predicted yielding probability is 87.5%, while that of pressing the button was 96.3%. Practical applications: Traffic engineers and planners may use these findings to improve the safety of crosswalk users.},
 bibtype = {article},
 author = {Kutela, Boniphace and Teng, Hualiang},
 doi = {10.1016/J.JSR.2019.02.008},
 journal = {Journal of Safety Research}
}

@article{
 title = {Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes},
 type = {article},
 year = {2019},
 pages = {46},
 volume = {3},
 websites = {https://www.mdpi.com/2504-2289/3/3/46},
 month = {7},
 day = {31},
 id = {3f7937c2-97f5-3237-8dc9-009449e1e600},
 created = {2019-08-03T12:08:10.546Z},
 accessed = {2019-08-03},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2019-08-03T12:08:10.635Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {<p>Artificial intelligence-enabled adaptive learning systems (AI-ALS) have been increasingly utilized in education. Schools are usually afforded the freedom to deploy the AI-ALS that they prefer. However, even before artificial intelligence autonomously develops into artificial superintelligence in the future, it would be remiss to entirely leave the students to the AI-ALS without any independent oversight of the potential issues. For example, if the students score well in formative assessments within the AI-ALS but subsequently perform badly in paper-based post-tests, or if the relentless algorithm of a particular AI-ALS is suspected of causing undue stress for the students, they should be addressed by educational stakeholders. Policy makers and educational stakeholders should collaborate to analyze the data from multiple AI-ALS deployed in different schools to achieve strategic oversight. The current paper provides exemplars to illustrate how this future-ready strategic oversight could be implemented using an artificial intelligence-based Bayesian network software to analyze the data from five dissimilar AI-ALS, each deployed in a different school. Besides using descriptive analytics to reveal potential issues experienced by students within each AI-ALS, this human-centric AI-empowered approach also enables explainable predictive analytics of the students’ learning outcomes in paper-based summative assessments after training is completed in each AI-ALS.</p>},
 bibtype = {article},
 author = {HOW, undefined},
 doi = {10.3390/bdcc3030046},
 journal = {Big Data and Cognitive Computing},
 number = {3}
}

@inbook{
 type = {inbook},
 year = {2020},
 pages = {629-640},
 websites = {http://link.springer.com/10.1007/978-981-13-8676-3_53},
 publisher = {Springer, Singapore},
 id = {748c9768-dd58-364d-b98e-a054acf6f2ec},
 created = {2019-08-22T13:44:38.849Z},
 accessed = {2019-08-22},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2019-08-22T13:44:38.950Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {inbook},
 author = {Pati, Dakshya P. and Panda, Sucheta},
 doi = {10.1007/978-981-13-8676-3_53},
 chapter = {A Comprehensive Review on Cancer Detection and Prediction Using Computational Methods}
}

@inproceedings{
 title = {A Bayesian Network Model for Real-time Crash Prediction Based on Selected Variables by Random Forest},
 type = {inproceedings},
 year = {2019},
 pages = {670-677},
 websites = {https://ieeexplore.ieee.org/document/8883694/},
 month = {7},
 publisher = {IEEE},
 id = {6243ec4c-b668-3702-9e59-cf0cd1279f02},
 created = {2019-11-06T23:01:57.995Z},
 accessed = {2019-11-06},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2019-11-06T23:01:57.995Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Wu, Mingxian and Shan, Donghui and Wang, Zuo and Sun, Xiaoduan and Liu, Jianbei and Sun, Ming},
 doi = {10.1109/ICTIS.2019.8883694},
 booktitle = {2019 5th International Conference on Transportation Information and Safety (ICTIS)}
}

@article{
 title = {Bayesian Network Classifiers},
 type = {article},
 year = {1997},
 pages = {131-163},
 volume = {29},
 websites = {http://dx.doi.org/10.1023/A:1007465528199%5Cnpapers2://publication/doi/10.1023/A:1007465528199},
 id = {46fa1577-f67d-3e80-bbdd-4bccc555eee0},
 created = {2019-12-27T22:41:19.683Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2019-12-27T22:41:19.683Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4. 5. This fact raises the question of ...},
 bibtype = {article},
 author = {Friedman, Nir and Geiger, Dan and Goldszmidt, Moises},
 doi = {10.1023/A:1007465528199},
 journal = {Machine learning}
}

@article{
 title = {Estimation and Application of a Bayesian Network Model for Discrete Travel Choice Analysis},
 type = {article},
 id = {56632ecf-ec53-36d3-9c3d-19ed3edf4ce4},
 created = {2020-01-06T20:18:13.290Z},
 accessed = {2020-01-06},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2020-01-06T20:18:13.378Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Xie, Chi},
 journal = {Transportation Letters}
}

@article{
 title = {Dynamic Bayesian network for crop growth prediction in greenhouses},
 type = {article},
 year = {2020},
 pages = {105167},
 volume = {169},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S0168169919321131},
 month = {2},
 id = {f33444c1-42a6-3c50-b8e4-26aaa7bd7341},
 created = {2020-01-11T19:54:57.882Z},
 accessed = {2020-01-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2020-01-11T19:54:57.966Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Kocian, A. and Massa, D. and Cannazzaro, S. and Incrocci, L. and Di Lonardo, S. and Milazzo, P. and Chessa, S.},
 doi = {10.1016/j.compag.2019.105167},
 journal = {Computers and Electronics in Agriculture}
}

@article{
 title = {Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network},
 type = {article},
 year = {2020},
 pages = {436-443},
 websites = {https://ascopubs.org/doi/10.1200/CCI.19.00136},
 month = {5},
 id = {2b87dcae-451e-35f3-8587-280f7a10ca61},
 created = {2020-05-20T23:04:01.392Z},
 accessed = {2020-05-20},
 file_attached = {true},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},
 last_modified = {2020-05-20T23:04:01.484Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Sieswerda, Melle S. and Bermejo, Inigo and Geleijnse, Gijs and Aarts, Mieke J. and Lemmens, Valery E.P.P. and De Ruysscher, Dirk and Dekker, André L.A.J. and Verbeek, Xander A.A.M},
 doi = {10.1200/CCI.19.00136},
 journal = {JCO Clinical Cancer Informatics},
 number = {4}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! Next you can\n      <a href=\"/network/register?next=/network/newSiteFromTemplate%3Fbiburl=\"\n      >create a new website with this list</a>, or\n      embed it in an existing web page by copying &amp; pasting\n      any of the following snippets.\n\n      <div style=\"text-align: left; margin-top: 1em;\">\n        <strong>JavaScript</strong>\n        <span class=\"comment recommended\">(easiest)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;script src=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/group/379f32c7-d27b-3627-898a-9a4acebc265b?jsonp=1&amp;jsonp=1\"&gt;&lt;/script&gt;\n          </code>\n        </div>\n\n        <strong>PHP</strong>\n        <div class=\"well well-small\">\n          <code>\n            &lt;?php\n            $contents = file_get_contents(\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/group/379f32c7-d27b-3627-898a-9a4acebc265b?jsonp=1\");\n            print_r($contents);\n            ?&gt;\n          </code>\n        </div>\n\n        <strong>iFrame</strong>\n        <span class=\"comment\">(not recommended)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;iframe src=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/group/379f32c7-d27b-3627-898a-9a4acebc265b?jsonp=1\"&gt;&lt;/iframe&gt;\n          </code>\n        </div>\n\n        <p>\n          For more details see the <a href=\"//bibbase.org/help\">documention</a>.\n        </p>\n      </div>\n    </div>\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_preview\"\n         style=\"display: none;\">\n\n      This is a preview! To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"pati-panda--2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-981-13-8676-3_53\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pati-panda--2020', 'http://link.springer.com/10.1007/978-981-13-8676-3_53', event);\">\n    A Comprehensive Review on Cancer Detection and Prediction Using Computational Methods.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pati,  D., P.;  and Panda,  S.\n</span>\n\n  \n\n</span>\n\n  pages 629-640. Springer, Singapore,  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-981-13-8676-3_53\"\n     onclick=\"log_download('pati-panda--2020', 'http://link.springer.com/10.1007/978-981-13-8676-3_53', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\" [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-981-13-8676-3_53\"\n     onclick=\"log_download('pati-panda--2020', 'http://doi.org/10.1007/978-981-13-8676-3_53', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pati-panda--2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pati-panda--2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2020},\n pages = {629-640},\n websites = {http://link.springer.com/10.1007/978-981-13-8676-3_53},\n publisher = {Springer, Singapore},\n id = {748c9768-dd58-364d-b98e-a054acf6f2ec},\n created = {2019-08-22T13:44:38.849Z},\n accessed = {2019-08-22},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2019-08-22T13:44:38.950Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {inbook},\n author = {Pati, Dakshya P. and Panda, Sucheta},\n doi = {10.1007/978-981-13-8676-3_53},\n chapter = {A Comprehensive Review on Cancer Detection and Prediction Using Computational Methods}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kocian-massa-cannazzaro-incrocci-dilonardo-milazzo-chessa-dynamicbayesiannetworkforcropgrowthpredictioningreenhouses-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0168169919321131\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kocian-massa-cannazzaro-incrocci-dilonardo-milazzo-chessa-dynamicbayesiannetworkforcropgrowthpredictioningreenhouses-2020', 'https://linkinghub.elsevier.com/retrieve/pii/S0168169919321131', event);\">\n    Dynamic Bayesian network for crop growth prediction in greenhouses.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kocian,  A.; Massa,  D.; Cannazzaro,  S.; Incrocci,  L.; Di Lonardo,  S.; Milazzo,  P.;  and Chessa,  S.\n</span>\n\n  \n\n</span>\n\n  <i>Computers and Electronics in Agriculture</i>, 169: 105167. 2 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0168169919321131\"\n     onclick=\"log_download('kocian-massa-cannazzaro-incrocci-dilonardo-milazzo-chessa-dynamicbayesiannetworkforcropgrowthpredictioningreenhouses-2020', 'https://linkinghub.elsevier.com/retrieve/pii/S0168169919321131', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Dynamic Bayesian network for crop growth prediction in greenhouses [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.compag.2019.105167\"\n     onclick=\"log_download('kocian-massa-cannazzaro-incrocci-dilonardo-milazzo-chessa-dynamicbayesiannetworkforcropgrowthpredictioningreenhouses-2020', 'http://doi.org/10.1016/j.compag.2019.105167', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kocian-massa-cannazzaro-incrocci-dilonardo-milazzo-chessa-dynamicbayesiannetworkforcropgrowthpredictioningreenhouses-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kocian-massa-cannazzaro-incrocci-dilonardo-milazzo-chessa-dynamicbayesiannetworkforcropgrowthpredictioningreenhouses-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Dynamic Bayesian network for crop growth prediction in greenhouses},\n type = {article},\n year = {2020},\n pages = {105167},\n volume = {169},\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0168169919321131},\n month = {2},\n id = {f33444c1-42a6-3c50-b8e4-26aaa7bd7341},\n created = {2020-01-11T19:54:57.882Z},\n accessed = {2020-01-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2020-01-11T19:54:57.966Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Kocian, A. and Massa, D. and Cannazzaro, S. and Incrocci, L. and Di Lonardo, S. and Milazzo, P. and Chessa, S.},\n doi = {10.1016/j.compag.2019.105167},\n journal = {Computers and Electronics in Agriculture}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sieswerda-bermejo-geleijnse-aarts-lemmens-deruysscher-dekker-verbeek-predictinglungcancersurvivalusingprobabilisticreclassificationoftnmeditionswithabayesiannetwork-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/3bce4eca-8ec5-1f05-615c-7c0c8d197cd9/full_text.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sieswerda-bermejo-geleijnse-aarts-lemmens-deruysscher-dekker-verbeek-predictinglungcancersurvivalusingprobabilisticreclassificationoftnmeditionswithabayesiannetwork-2020', 'https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/3bce4eca-8ec5-1f05-615c-7c0c8d197cd9/full_text.pdf.pdf', event);\">\n    Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sieswerda,  M., S.; Bermejo,  I.; Geleijnse,  G.; Aarts,  M., J.; Lemmens,  V., E.; De Ruysscher,  D.; Dekker,  A., L.;  and Verbeek,  X., A.\n</span>\n\n  \n\n</span>\n\n  <i>JCO Clinical Cancer Informatics</i>, (4): 436-443. 5 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/3bce4eca-8ec5-1f05-615c-7c0c8d197cd9/full_text.pdf.pdf\"\n     onclick=\"log_download('sieswerda-bermejo-geleijnse-aarts-lemmens-deruysscher-dekker-verbeek-predictinglungcancersurvivalusingprobabilisticreclassificationoftnmeditionswithabayesiannetwork-2020', 'https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/3bce4eca-8ec5-1f05-615c-7c0c8d197cd9/full_text.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://ascopubs.org/doi/10.1200/CCI.19.00136\"\n     onclick=\"log_download('sieswerda-bermejo-geleijnse-aarts-lemmens-deruysscher-dekker-verbeek-predictinglungcancersurvivalusingprobabilisticreclassificationoftnmeditionswithabayesiannetwork-2020', 'https://ascopubs.org/doi/10.1200/CCI.19.00136', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1200/CCI.19.00136\"\n     onclick=\"log_download('sieswerda-bermejo-geleijnse-aarts-lemmens-deruysscher-dekker-verbeek-predictinglungcancersurvivalusingprobabilisticreclassificationoftnmeditionswithabayesiannetwork-2020', 'http://doi.org/10.1200/CCI.19.00136', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sieswerda-bermejo-geleijnse-aarts-lemmens-deruysscher-dekker-verbeek-predictinglungcancersurvivalusingprobabilisticreclassificationoftnmeditionswithabayesiannetwork-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sieswerda-bermejo-geleijnse-aarts-lemmens-deruysscher-dekker-verbeek-predictinglungcancersurvivalusingprobabilisticreclassificationoftnmeditionswithabayesiannetwork-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network},\n type = {article},\n year = {2020},\n pages = {436-443},\n websites = {https://ascopubs.org/doi/10.1200/CCI.19.00136},\n month = {5},\n id = {2b87dcae-451e-35f3-8587-280f7a10ca61},\n created = {2020-05-20T23:04:01.392Z},\n accessed = {2020-05-20},\n file_attached = {true},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2020-05-20T23:04:01.484Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Sieswerda, Melle S. and Bermejo, Inigo and Geleijnse, Gijs and Aarts, Mieke J. and Lemmens, Valery E.P.P. and De Ruysscher, Dirk and Dekker, André L.A.J. and Verbeek, Xander A.A.M},\n doi = {10.1200/CCI.19.00136},\n journal = {JCO Clinical Cancer Informatics},\n number = {4}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"kutela-teng-predictionofdriversandpedestriansbehaviorsatsignalizedmidblockdanishoffsetcrosswalksusingbayesiannetworks-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0022437518306595?dgcid=author#!\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kutela-teng-predictionofdriversandpedestriansbehaviorsatsignalizedmidblockdanishoffsetcrosswalksusingbayesiannetworks-2019', 'https://www.sciencedirect.com/science/article/pii/S0022437518306595?dgcid=author#!', event);\">\n    Prediction of drivers and pedestrians' behaviors at signalized mid-block Danish offset crosswalks using Bayesian networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kutela,  B.;  and Teng,  H.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Safety Research</i>, 69: 75-83. 6 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0022437518306595?dgcid=author#!\"\n     onclick=\"log_download('kutela-teng-predictionofdriversandpedestriansbehaviorsatsignalizedmidblockdanishoffsetcrosswalksusingbayesiannetworks-2019', 'https://www.sciencedirect.com/science/article/pii/S0022437518306595?dgcid=author#!', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Prediction of drivers and pedestrians&#x27; behaviors at signalized mid-block Danish offset crosswalks using Bayesian networks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/J.JSR.2019.02.008\"\n     onclick=\"log_download('kutela-teng-predictionofdriversandpedestriansbehaviorsatsignalizedmidblockdanishoffsetcrosswalksusingbayesiannetworks-2019', 'http://doi.org/10.1016/J.JSR.2019.02.008', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kutela-teng-predictionofdriversandpedestriansbehaviorsatsignalizedmidblockdanishoffsetcrosswalksusingbayesiannetworks-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kutela-teng-predictionofdriversandpedestriansbehaviorsatsignalizedmidblockdanishoffsetcrosswalksusingbayesiannetworks-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Prediction of drivers and pedestrians&#x27; behaviors at signalized mid-block Danish offset crosswalks using Bayesian networks},\n type = {article},\n year = {2019},\n pages = {75-83},\n volume = {69},\n websites = {https://www.sciencedirect.com/science/article/pii/S0022437518306595?dgcid=author#!},\n month = {6},\n publisher = {Pergamon},\n day = {1},\n id = {0b551964-6737-3715-9f19-89171b033552},\n created = {2019-03-23T04:04:22.832Z},\n accessed = {2019-03-23},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2019-03-23T04:04:22.918Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Introduction: This study presents the prediction of driver yielding compliance and pedestrian tendencies to press pushbuttons at signalized mid-block Danish offset crosswalks. Method: It applies Bayesian Networks (BNs) analysis, which is basically a graphical non-functional form model, on observational survey data collected from five signalized crosswalks in Las Vegas, Nevada. The BNs structures were found in the data by applying several score functions. By considering prediction accuracy and the Area under the Receiver Operating Characteristic (ROC) curves, the BN found that using the Bayesian Information Criterion (BIC) score resulted in the best network structure, compared to the ones found using K2 and the Akaike Information Criterion (AIC). The BIC score-based structure was then used for parameter learning and probabilistic inference. Results: Results show that, when considering an individual scenario, the highest predicted yielding compliance (81%) is attained when pedestrians arrive at the crosswalk while the flashes are active, whereas the lowest predicted yielding compliance (23.4%) is observed when the pedestrians cross between the yield line and advanced pedestrian crosswalk sign. On the other hand, crossing within marked stripes, approaching the crosswalk from the near side of the pushbutton pole, inactive flashing lights, and being the first to arrive at the crosswalk result in relatively high-predicted probabilities of pedestrians pressing pushbutton. Furthermore, with a combination of scenarios, the maximum achievable predicted yielding probability is 87.5%, while that of pressing the button was 96.3%. Practical applications: Traffic engineers and planners may use these findings to improve the safety of crosswalk users.},\n bibtype = {article},\n author = {Kutela, Boniphace and Teng, Hualiang},\n doi = {10.1016/J.JSR.2019.02.008},\n journal = {Journal of Safety Research}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Introduction: This study presents the prediction of driver yielding compliance and pedestrian tendencies to press pushbuttons at signalized mid-block Danish offset crosswalks. Method: It applies Bayesian Networks (BNs) analysis, which is basically a graphical non-functional form model, on observational survey data collected from five signalized crosswalks in Las Vegas, Nevada. The BNs structures were found in the data by applying several score functions. By considering prediction accuracy and the Area under the Receiver Operating Characteristic (ROC) curves, the BN found that using the Bayesian Information Criterion (BIC) score resulted in the best network structure, compared to the ones found using K2 and the Akaike Information Criterion (AIC). The BIC score-based structure was then used for parameter learning and probabilistic inference. Results: Results show that, when considering an individual scenario, the highest predicted yielding compliance (81%) is attained when pedestrians arrive at the crosswalk while the flashes are active, whereas the lowest predicted yielding compliance (23.4%) is observed when the pedestrians cross between the yield line and advanced pedestrian crosswalk sign. On the other hand, crossing within marked stripes, approaching the crosswalk from the near side of the pushbutton pole, inactive flashing lights, and being the first to arrive at the crosswalk result in relatively high-predicted probabilities of pedestrians pressing pushbutton. Furthermore, with a combination of scenarios, the maximum achievable predicted yielding probability is 87.5%, while that of pressing the button was 96.3%. Practical applications: Traffic engineers and planners may use these findings to improve the safety of crosswalk users.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"how-futurereadystrategicoversightofmultipleartificialsuperintelligenceenabledadaptivelearningsystemsviahumancentricexplainableaiempoweredpredictiveoptimizationsofeducationaloutcomes-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.mdpi.com/2504-2289/3/3/46\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'how-futurereadystrategicoversightofmultipleartificialsuperintelligenceenabledadaptivelearningsystemsviahumancentricexplainableaiempoweredpredictiveoptimizationsofeducationaloutcomes-2019', 'https://www.mdpi.com/2504-2289/3/3/46', event);\">\n    Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  HOW\n</span>\n\n  \n\n</span>\n\n  <i>Big Data and Cognitive Computing</i>, 3(3): 46. 7 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.mdpi.com/2504-2289/3/3/46\"\n     onclick=\"log_download('how-futurereadystrategicoversightofmultipleartificialsuperintelligenceenabledadaptivelearningsystemsviahumancentricexplainableaiempoweredpredictiveoptimizationsofeducationaloutcomes-2019', 'https://www.mdpi.com/2504-2289/3/3/46', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.3390/bdcc3030046\"\n     onclick=\"log_download('how-futurereadystrategicoversightofmultipleartificialsuperintelligenceenabledadaptivelearningsystemsviahumancentricexplainableaiempoweredpredictiveoptimizationsofeducationaloutcomes-2019', 'http://doi.org/10.3390/bdcc3030046', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/how-futurereadystrategicoversightofmultipleartificialsuperintelligenceenabledadaptivelearningsystemsviahumancentricexplainableaiempoweredpredictiveoptimizationsofeducationaloutcomes-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('how-futurereadystrategicoversightofmultipleartificialsuperintelligenceenabledadaptivelearningsystemsviahumancentricexplainableaiempoweredpredictiveoptimizationsofeducationaloutcomes-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes},\n type = {article},\n year = {2019},\n pages = {46},\n volume = {3},\n websites = {https://www.mdpi.com/2504-2289/3/3/46},\n month = {7},\n day = {31},\n id = {3f7937c2-97f5-3237-8dc9-009449e1e600},\n created = {2019-08-03T12:08:10.546Z},\n accessed = {2019-08-03},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2019-08-03T12:08:10.635Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {&lt;p&gt;Artificial intelligence-enabled adaptive learning systems (AI-ALS) have been increasingly utilized in education. Schools are usually afforded the freedom to deploy the AI-ALS that they prefer. However, even before artificial intelligence autonomously develops into artificial superintelligence in the future, it would be remiss to entirely leave the students to the AI-ALS without any independent oversight of the potential issues. For example, if the students score well in formative assessments within the AI-ALS but subsequently perform badly in paper-based post-tests, or if the relentless algorithm of a particular AI-ALS is suspected of causing undue stress for the students, they should be addressed by educational stakeholders. Policy makers and educational stakeholders should collaborate to analyze the data from multiple AI-ALS deployed in different schools to achieve strategic oversight. The current paper provides exemplars to illustrate how this future-ready strategic oversight could be implemented using an artificial intelligence-based Bayesian network software to analyze the data from five dissimilar AI-ALS, each deployed in a different school. Besides using descriptive analytics to reveal potential issues experienced by students within each AI-ALS, this human-centric AI-empowered approach also enables explainable predictive analytics of the students’ learning outcomes in paper-based summative assessments after training is completed in each AI-ALS.&lt;/p&gt;},\n bibtype = {article},\n author = {HOW, undefined},\n doi = {10.3390/bdcc3030046},\n journal = {Big Data and Cognitive Computing},\n number = {3}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  <p>Artificial intelligence-enabled adaptive learning systems (AI-ALS) have been increasingly utilized in education. Schools are usually afforded the freedom to deploy the AI-ALS that they prefer. However, even before artificial intelligence autonomously develops into artificial superintelligence in the future, it would be remiss to entirely leave the students to the AI-ALS without any independent oversight of the potential issues. For example, if the students score well in formative assessments within the AI-ALS but subsequently perform badly in paper-based post-tests, or if the relentless algorithm of a particular AI-ALS is suspected of causing undue stress for the students, they should be addressed by educational stakeholders. Policy makers and educational stakeholders should collaborate to analyze the data from multiple AI-ALS deployed in different schools to achieve strategic oversight. The current paper provides exemplars to illustrate how this future-ready strategic oversight could be implemented using an artificial intelligence-based Bayesian network software to analyze the data from five dissimilar AI-ALS, each deployed in a different school. Besides using descriptive analytics to reveal potential issues experienced by students within each AI-ALS, this human-centric AI-empowered approach also enables explainable predictive analytics of the students’ learning outcomes in paper-based summative assessments after training is completed in each AI-ALS.</p>\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wu-shan-wang-sun-liu-sun-abayesiannetworkmodelforrealtimecrashpredictionbasedonselectedvariablesbyrandomforest-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/8883694/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wu-shan-wang-sun-liu-sun-abayesiannetworkmodelforrealtimecrashpredictionbasedonselectedvariablesbyrandomforest-2019', 'https://ieeexplore.ieee.org/document/8883694/', event);\">\n    A Bayesian Network Model for Real-time Crash Prediction Based on Selected Variables by Random Forest.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wu,  M.; Shan,  D.; Wang,  Z.; Sun,  X.; Liu,  J.;  and Sun,  M.\n</span>\n\n  \n\n</span>\n\n  In <i>2019 5th International Conference on Transportation Information and Safety (ICTIS)</i>, pages 670-677, 7 2019. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8883694/\"\n     onclick=\"log_download('wu-shan-wang-sun-liu-sun-abayesiannetworkmodelforrealtimecrashpredictionbasedonselectedvariablesbyrandomforest-2019', 'https://ieeexplore.ieee.org/document/8883694/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Bayesian Network Model for Real-time Crash Prediction Based on Selected Variables by Random Forest [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ICTIS.2019.8883694\"\n     onclick=\"log_download('wu-shan-wang-sun-liu-sun-abayesiannetworkmodelforrealtimecrashpredictionbasedonselectedvariablesbyrandomforest-2019', 'http://doi.org/10.1109/ICTIS.2019.8883694', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wu-shan-wang-sun-liu-sun-abayesiannetworkmodelforrealtimecrashpredictionbasedonselectedvariablesbyrandomforest-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wu-shan-wang-sun-liu-sun-abayesiannetworkmodelforrealtimecrashpredictionbasedonselectedvariablesbyrandomforest-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {A Bayesian Network Model for Real-time Crash Prediction Based on Selected Variables by Random Forest},\n type = {inproceedings},\n year = {2019},\n pages = {670-677},\n websites = {https://ieeexplore.ieee.org/document/8883694/},\n month = {7},\n publisher = {IEEE},\n id = {6243ec4c-b668-3702-9e59-cf0cd1279f02},\n created = {2019-11-06T23:01:57.995Z},\n accessed = {2019-11-06},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2019-11-06T23:01:57.995Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Wu, Mingxian and Shan, Donghui and Wang, Zuo and Sun, Xiaoduan and Liu, Jianbei and Sun, Ming},\n doi = {10.1109/ICTIS.2019.8883694},\n booktitle = {2019 5th International Conference on Transportation Information and Safety (ICTIS)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis-2018', 'http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf', event);\">\n    Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Venkatesh,  T.\n</span>\n\n  \n\n</span>\n\n  .  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf\"\n     onclick=\"log_download('venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis-2018', 'http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis [pdf]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis},\n type = {article},\n year = {2018},\n websites = {http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf},\n id = {5b5c1742-115d-3d0d-9205-dae3a9e85e95},\n created = {2018-03-31T21:53:37.316Z},\n accessed = {2018-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2018-03-31T23:54:10.489Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {— The aim of the investigation was to analyse patient clinical data of Bronchiectasis patients in a Singaporean cohort in order to identify any unknown correlations between clinical parameters such as age, sex, presence of certain organisms in body etc. Both supervised learning and unsupervised machine learning computer algorithms were applied to the data set. A strong inverse correlation was found between the A.fumigatus burden level and P.aeruginosa status in the pulmonary track of patients. Differences in cytokine expressions in the body, which were found to be statistically significant, were also observed, and this could be used as a more accurate biomarker to flag Aspergillus as compared to the poorly accurate culture test which is currently used. Future research needs to be conducted to further investigate the relationship between A.fumigatus and P.aeruginosa and the viability of using the identified cytokines as biomarkers.},\n bibtype = {article},\n author = {Venkatesh, Tejas}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  — The aim of the investigation was to analyse patient clinical data of Bronchiectasis patients in a Singaporean cohort in order to identify any unknown correlations between clinical parameters such as age, sex, presence of certain organisms in body etc. Both supervised learning and unsupervised machine learning computer algorithms were applied to the data set. A strong inverse correlation was found between the A.fumigatus burden level and P.aeruginosa status in the pulmonary track of patients. Differences in cytokine expressions in the body, which were found to be statistically significant, were also observed, and this could be used as a more accurate biomarker to flag Aspergillus as compared to the poorly accurate culture test which is currently used. Future research needs to be conducted to further investigate the relationship between A.fumigatus and P.aeruginosa and the viability of using the identified cytokines as biomarkers.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wang-wang-liu-liu-wang-reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://journals.sagepub.com/doi/pdf/10.1177/1687814017747708\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wang-wang-liu-liu-wang-reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork-2018', 'http://journals.sagepub.com/doi/pdf/10.1177/1687814017747708', event);\">\n    Reverse deduction of vehicle group situation based on dynamic Bayesian network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang,  X.; Wang,  J.; Liu,  Y.; Liu,  Z.;  and Wang,  J.\n</span>\n\n  \n\n</span>\n\n  <i>Special Issue Article Advances in Mechanical Engineering</i>, 10(3): 1-15.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://journals.sagepub.com/doi/pdf/10.1177/1687814017747708\"\n     onclick=\"log_download('wang-wang-liu-liu-wang-reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork-2018', 'http://journals.sagepub.com/doi/pdf/10.1177/1687814017747708', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Reverse deduction of vehicle group situation based on dynamic Bayesian network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1177/1687814017747708\"\n     onclick=\"log_download('wang-wang-liu-liu-wang-reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork-2018', 'http://doi.org/10.1177/1687814017747708', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-wang-liu-liu-wang-reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-wang-liu-liu-wang-reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Reverse deduction of vehicle group situation based on dynamic Bayesian network},\n type = {article},\n year = {2018},\n keywords = {Poisson&#x27;s distribution,Vehicle group situation,dynamic Bayesian network,privacy protection,reverse deduction},\n pages = {1-15},\n volume = {10},\n websites = {http://journals.sagepub.com/doi/pdf/10.1177/1687814017747708},\n id = {fd96fbef-7906-3746-a462-6a5cb1829191},\n created = {2018-03-31T23:32:39.494Z},\n accessed = {2018-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2018-03-31T23:54:10.482Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Vehicle group is the basic unit of microscopic traffic flow, and also a concept that often involved in the research of active vehicle security. It is of great significance to identify vehicle group situation accurately for the research of traffic flow the-ory and the intelligent vehicle driving system. Three-lane condition was taken as an example and the privacy protection of driver (only the data of travel time were used) was a premise in this article. Poisson&#x27;s distribution was used to identify vehicle group situation which was constituted by target vehicle and its neighboring vehicles when the target vehicle arrived at the end of study area. And the dynamic Bayesian network was used to build the reverse deduction model of vehicle group situation. The model was verified through actual and virtual driving experiments. Verification results showed that the model established in this article was reasonable and feasible.},\n bibtype = {article},\n author = {Wang, Xiaoyuan and Wang, Jianqiang and Liu, Yaqi and Liu, Zhenxue and Wang, Jingheng},\n doi = {10.1177/1687814017747708},\n journal = {Special Issue Article Advances in Mechanical Engineering},\n number = {3}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Vehicle group is the basic unit of microscopic traffic flow, and also a concept that often involved in the research of active vehicle security. It is of great significance to identify vehicle group situation accurately for the research of traffic flow the-ory and the intelligent vehicle driving system. Three-lane condition was taken as an example and the privacy protection of driver (only the data of travel time were used) was a premise in this article. Poisson's distribution was used to identify vehicle group situation which was constituted by target vehicle and its neighboring vehicles when the target vehicle arrived at the end of study area. And the dynamic Bayesian network was used to build the reverse deduction model of vehicle group situation. The model was verified through actual and virtual driving experiments. Verification results showed that the model established in this article was reasonable and feasible.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"devi-priya-akhila-vasundhara-analysisandpredictionofstudentplacementforimprovingtheeducationstandards-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Analysis and prediction of student placement for improving the education standards.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Devi,  S., A.; Priya,  M., V.; Akhila,  P.;  and Vasundhara,  N.\n</span>\n\n  \n\n</span>\n\n  , 7: 303-306.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/devi-priya-akhila-vasundhara-analysisandpredictionofstudentplacementforimprovingtheeducationstandards-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('devi-priya-akhila-vasundhara-analysisandpredictionofstudentplacementforimprovingtheeducationstandards-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Analysis and prediction of student placement for improving the education standards},\n type = {article},\n year = {2018},\n keywords = {academics,association rules,decision tree,j48 algorithm,placement,prediction,universities},\n pages = {303-306},\n volume = {7},\n id = {75742eca-76db-39d0-b3ba-ea4502c587e8},\n created = {2018-03-31T23:54:09.395Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2018-03-31T23:54:09.395Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Devi, S Anjali and Priya, M Vishnu and Akhila, P and Vasundhara, N}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"rovinelli-sangid-proudhon-ludwig-usingmachinelearningandadatadrivenapproachtoidentifythesmallfatiguecrackdrivingforceinpolycrystallinematerials-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.nature.com/articles/s41524-018-0094-7\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'rovinelli-sangid-proudhon-ludwig-usingmachinelearningandadatadrivenapproachtoidentifythesmallfatiguecrackdrivingforceinpolycrystallinematerials-2018', 'http://www.nature.com/articles/s41524-018-0094-7', event);\">\n    Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Rovinelli,  A.; Sangid,  M., D.; Proudhon,  H.;  and Ludwig,  W.\n</span>\n\n  \n\n</span>\n\n  <i>npj Computational Materials</i>, 4(1): 35. 12 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.nature.com/articles/s41524-018-0094-7\"\n     onclick=\"log_download('rovinelli-sangid-proudhon-ludwig-usingmachinelearningandadatadrivenapproachtoidentifythesmallfatiguecrackdrivingforceinpolycrystallinematerials-2018', 'http://www.nature.com/articles/s41524-018-0094-7', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1038/s41524-018-0094-7\"\n     onclick=\"log_download('rovinelli-sangid-proudhon-ludwig-usingmachinelearningandadatadrivenapproachtoidentifythesmallfatiguecrackdrivingforceinpolycrystallinematerials-2018', 'http://doi.org/10.1038/s41524-018-0094-7', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/rovinelli-sangid-proudhon-ludwig-usingmachinelearningandadatadrivenapproachtoidentifythesmallfatiguecrackdrivingforceinpolycrystallinematerials-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('rovinelli-sangid-proudhon-ludwig-usingmachinelearningandadatadrivenapproachtoidentifythesmallfatiguecrackdrivingforceinpolycrystallinematerials-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials},\n type = {article},\n year = {2018},\n keywords = {Mechanical properties,Metals and alloys},\n pages = {35},\n volume = {4},\n websites = {http://www.nature.com/articles/s41524-018-0094-7},\n month = {12},\n publisher = {Nature Publishing Group},\n day = {16},\n id = {dc81e604-655d-3c91-a4b7-d70394916669},\n created = {2018-07-23T01:03:03.039Z},\n accessed = {2018-07-22},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2018-07-23T01:03:03.039Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature. Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics.},\n bibtype = {article},\n author = {Rovinelli, Andrea and Sangid, Michael D. and Proudhon, Henry and Ludwig, Wolfgang},\n doi = {10.1038/s41524-018-0094-7},\n journal = {npj Computational Materials},\n number = {1}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature. Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2015\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2015')\"\n      onkeypress=\"toggleGroup('group_2015')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2015</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"fernandes-irigoien-lozano-inza-goikoetxea-prez-evaluatingmachinelearningtechniquesforrecruitmentforecastingofsevennortheastatlanticfishspecies-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1574954114001563\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'fernandes-irigoien-lozano-inza-goikoetxea-prez-evaluatingmachinelearningtechniquesforrecruitmentforecastingofsevennortheastatlanticfishspecies-2015', 'http://www.sciencedirect.com/science/article/pii/S1574954114001563', event);\">\n    Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fernandes,  J., A.; Irigoien,  X.; Lozano,  J., A.; Inza,  I.; Goikoetxea,  N.;  and Pérez,  A.\n</span>\n\n  \n\n</span>\n\n  <i>Ecological Informatics</i>, 25: 35-42. 1 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1574954114001563\"\n     onclick=\"log_download('fernandes-irigoien-lozano-inza-goikoetxea-prez-evaluatingmachinelearningtechniquesforrecruitmentforecastingofsevennortheastatlanticfishspecies-2015', 'http://www.sciencedirect.com/science/article/pii/S1574954114001563', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ecoinf.2014.11.004\"\n     onclick=\"log_download('fernandes-irigoien-lozano-inza-goikoetxea-prez-evaluatingmachinelearningtechniquesforrecruitmentforecastingofsevennortheastatlanticfishspecies-2015', 'http://doi.org/10.1016/j.ecoinf.2014.11.004', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fernandes-irigoien-lozano-inza-goikoetxea-prez-evaluatingmachinelearningtechniquesforrecruitmentforecastingofsevennortheastatlanticfishspecies-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fernandes-irigoien-lozano-inza-goikoetxea-prez-evaluatingmachinelearningtechniquesforrecruitmentforecastingofsevennortheastatlanticfishspecies-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species},\n type = {article},\n year = {2015},\n keywords = {Bayesian networks,Fisheries management,Kernel density estimation,Pelagic fish,Recruitment forecasting,Supervised classification},\n pages = {35-42},\n volume = {25},\n websites = {http://www.sciencedirect.com/science/article/pii/S1574954114001563},\n month = {1},\n id = {88652dfa-d3a0-3a29-a600-b67b9c00fa5c},\n created = {2015-04-12T18:44:12.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.},\n bibtype = {article},\n author = {Fernandes, Jose A. and Irigoien, Xabier and Lozano, Jose A. and Inza, Iñaki and Goikoetxea, Nerea and Pérez, Aritz},\n doi = {10.1016/j.ecoinf.2014.11.004},\n journal = {Ecological Informatics}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"jafarpour-izadi-precup-buckeridge-quantifyingthedeterminantsofoutbreakdetectionperformancethroughsimulationandmachinelearning-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046414002299\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'jafarpour-izadi-precup-buckeridge-quantifyingthedeterminantsofoutbreakdetectionperformancethroughsimulationandmachinelearning-2015', 'http://www.sciencedirect.com/science/article/pii/S1532046414002299', event);\">\n    Quantifying the determinants of outbreak detection performance through simulation and machine learning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Jafarpour,  N.; Izadi,  M.; Precup,  D.;  and Buckeridge,  D., L.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of biomedical informatics</i>, 53: 180-7. 2 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046414002299\"\n     onclick=\"log_download('jafarpour-izadi-precup-buckeridge-quantifyingthedeterminantsofoutbreakdetectionperformancethroughsimulationandmachinelearning-2015', 'http://www.sciencedirect.com/science/article/pii/S1532046414002299', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Quantifying the determinants of outbreak detection performance through simulation and machine learning. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jbi.2014.10.009\"\n     onclick=\"log_download('jafarpour-izadi-precup-buckeridge-quantifyingthedeterminantsofoutbreakdetectionperformancethroughsimulationandmachinelearning-2015', 'http://doi.org/10.1016/j.jbi.2014.10.009', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/jafarpour-izadi-precup-buckeridge-quantifyingthedeterminantsofoutbreakdetectionperformancethroughsimulationandmachinelearning-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('jafarpour-izadi-precup-buckeridge-quantifyingthedeterminantsofoutbreakdetectionperformancethroughsimulationandmachinelearning-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Quantifying the determinants of outbreak detection performance through simulation and machine learning.},\n type = {article},\n year = {2015},\n keywords = {Bayesian networks,Disease outbreak detection,Outbreak simulation,Predicting performance,Public health informatics,Surveillance},\n pages = {180-7},\n volume = {53},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046414002299},\n month = {2},\n id = {d7500789-9ac9-3544-924e-f336004042ac},\n created = {2015-04-12T18:51:29.000Z},\n accessed = {2015-03-17},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.\n\nMATERIALS AND METHODS: We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation.\n\nRESULTS: The model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns.\n\nCONCLUSION: We developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems.},\n bibtype = {article},\n author = {Jafarpour, Nastaran and Izadi, Masoumeh and Precup, Doina and Buckeridge, David L},\n doi = {10.1016/j.jbi.2014.10.009},\n journal = {Journal of biomedical informatics}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.\n\nMATERIALS AND METHODS: We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation.\n\nRESULTS: The model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns.\n\nCONCLUSION: We developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kourou-exarchos-exarchos-karamouzis-fotiadis-machinelearningapplicationsincancerprognosisandprediction-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S2001037014000464\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kourou-exarchos-exarchos-karamouzis-fotiadis-machinelearningapplicationsincancerprognosisandprediction-2015', 'http://www.sciencedirect.com/science/article/pii/S2001037014000464', event);\">\n    Machine learning applications in cancer prognosis and prediction.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kourou,  K.; Exarchos,  T., P.; Exarchos,  K., P.; Karamouzis,  M., V.;  and Fotiadis,  D., I.\n</span>\n\n  \n\n</span>\n\n  <i>Computational and Structural Biotechnology Journal</i>, 13: 8-17. 12 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S2001037014000464\"\n     onclick=\"log_download('kourou-exarchos-exarchos-karamouzis-fotiadis-machinelearningapplicationsincancerprognosisandprediction-2015', 'http://www.sciencedirect.com/science/article/pii/S2001037014000464', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Machine learning applications in cancer prognosis and prediction [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.csbj.2014.11.005\"\n     onclick=\"log_download('kourou-exarchos-exarchos-karamouzis-fotiadis-machinelearningapplicationsincancerprognosisandprediction-2015', 'http://doi.org/10.1016/j.csbj.2014.11.005', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kourou-exarchos-exarchos-karamouzis-fotiadis-machinelearningapplicationsincancerprognosisandprediction-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kourou-exarchos-exarchos-karamouzis-fotiadis-machinelearningapplicationsincancerprognosisandprediction-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Machine learning applications in cancer prognosis and prediction},\n type = {article},\n year = {2015},\n keywords = {ANN,AUC,Area Under Curve,Artificial Neural Network,BCRSVM,BN,Bayesian Network,Breast Cancer Support Vector Machine,CFS,Cancer recurrence,Cancer survival,Cancer susceptibility,Correlation based Feature Selection,DT,Decision Tree,ES,Early Stopping algorithm,GEO,Gene Expression Omnibus,HTT,High-throughput Technologies,LCS,Learning Classifying Systems,ML,Machine Learning,Machine learning,NCI caArray,NSCLC,National Cancer Institute Array Data Management Sy,Non-small Cell Lung Cancer,OSCC,Oral Squamous Cell Carcinoma,PPI,Predictive models,Protein–Protein Interaction,ROC,Receiver Operating Characteristic,SEER,SSL,SVM,Semi-supervised Learning,Support Vector Machine,Surveillance, Epidemiology and End results Databas,TCGA,The Cancer Genome Atlas Research Network},\n pages = {8-17},\n volume = {13},\n websites = {http://www.sciencedirect.com/science/article/pii/S2001037014000464},\n month = {12},\n id = {6549cc53-5dde-30fc-94cf-a29fdd5890b5},\n created = {2015-04-12T18:59:39.000Z},\n accessed = {2015-01-01},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.},\n bibtype = {article},\n author = {Kourou, Konstantina and Exarchos, Themis P. and Exarchos, Konstantinos P. and Karamouzis, Michalis V. and Fotiadis, Dimitrios I.},\n doi = {10.1016/j.csbj.2014.11.005},\n journal = {Computational and Structural Biotechnology Journal}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2014\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2014')\"\n      onkeypress=\"toggleGroup('group_2014')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2014</span>\n      <span class=\"bibbase_group_count\">\n        \n        (7)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0304380014000398\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014', 'http://www.sciencedirect.com/science/article/pii/S0304380014000398', event);\">\n    A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gieder,  K., D.; Karpanty,  S., M.; Fraser,  J., D.; Catlin,  D., H.; Gutierrez,  B., T.; Plant,  N., G.; Turecek,  A., M.;  and Robert Thieler,  E.\n</span>\n\n  \n\n</span>\n\n  <i>Ecological Modelling</i>, 276: 38-50. 3 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0304380014000398\"\n     onclick=\"log_download('gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014', 'http://www.sciencedirect.com/science/article/pii/S0304380014000398', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ecolmodel.2014.01.005\"\n     onclick=\"log_download('gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014', 'http://doi.org/10.1016/j.ecolmodel.2014.01.005', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features},\n type = {article},\n year = {2014},\n keywords = {Bayesian network,Development,Habitat,Piping plover,Sea-level rise,Shorebird},\n pages = {38-50},\n volume = {276},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380014000398},\n month = {3},\n id = {086f8466-6471-3150-a892-146a13a8d70a},\n created = {2015-04-12T18:51:29.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modeling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model&#x27;s dataset. We found that model predictions were more successful when the ranges of physical conditions included in model development were varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modeling impacts of sea-level rise or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.},\n bibtype = {article},\n author = {Gieder, Katherina D. and Karpanty, Sarah M. and Fraser, James D. and Catlin, Daniel H. and Gutierrez, Benjamin T. and Plant, Nathaniel G. and Turecek, Aaron M. and Robert Thieler, E.},\n doi = {10.1016/j.ecolmodel.2014.01.005},\n journal = {Ecological Modelling}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modeling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model's dataset. We found that model predictions were more successful when the ranges of physical conditions included in model development were varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modeling impacts of sea-level rise or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sandri-berchialla-baldi-gregori-deblasi-dynamicbayesiannetworkstopredictsequencesoforganfailuresinpatientsadmittedtoicu-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046413001998\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sandri-berchialla-baldi-gregori-deblasi-dynamicbayesiannetworkstopredictsequencesoforganfailuresinpatientsadmittedtoicu-2014', 'http://www.sciencedirect.com/science/article/pii/S1532046413001998', event);\">\n    Dynamic Bayesian Networks to predict sequences of organ failures in patients admitted to ICU.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sandri,  M.; Berchialla,  P.; Baldi,  I.; Gregori,  D.;  and De Blasi,  R., A.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of biomedical informatics</i>, 48: 106-13. 4 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046413001998\"\n     onclick=\"log_download('sandri-berchialla-baldi-gregori-deblasi-dynamicbayesiannetworkstopredictsequencesoforganfailuresinpatientsadmittedtoicu-2014', 'http://www.sciencedirect.com/science/article/pii/S1532046413001998', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Dynamic Bayesian Networks to predict sequences of organ failures in patients admitted to ICU. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jbi.2013.12.008\"\n     onclick=\"log_download('sandri-berchialla-baldi-gregori-deblasi-dynamicbayesiannetworkstopredictsequencesoforganfailuresinpatientsadmittedtoicu-2014', 'http://doi.org/10.1016/j.jbi.2013.12.008', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sandri-berchialla-baldi-gregori-deblasi-dynamicbayesiannetworkstopredictsequencesoforganfailuresinpatientsadmittedtoicu-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sandri-berchialla-baldi-gregori-deblasi-dynamicbayesiannetworkstopredictsequencesoforganfailuresinpatientsadmittedtoicu-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Dynamic Bayesian Networks to predict sequences of organ failures in patients admitted to ICU.},\n type = {article},\n year = {2014},\n keywords = {Aged,Algorithms,Bayes Theorem,Critical Illness,Decision Support Systems, Clinical,Female,Hospital Mortality,Humans,Intensive Care,Intensive Care Units,Intensive Care: methods,Length of Stay,Male,Middle Aged,Multiple Organ Failure,Multiple Organ Failure: physiopathology,Multiple Organ Failure: therapy,Probability,Prognosis,Software,Time Factors},\n pages = {106-13},\n volume = {48},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046413001998},\n month = {4},\n id = {009707e8-f54a-399f-95c0-b1de38f6d52a},\n created = {2015-04-12T20:17:32.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Multi Organ Dysfunction Syndrome (MODS) represents a continuum of physiologic derangements and is the major cause of death in the Intensive Care Unit (ICU). Scoring systems for organ failure have become an integral part of critical care practice and play an important role in ICU-based research by tracking disease progression and facilitating patient stratification based on evaluation of illness severity during ICU stay. In this study a Dynamic Bayesian Network (DBN) was applied to model SOFA severity score changes in 79 adult critically ill patients consecutively admitted to the general ICU of the Sant&#x27;Andrea University hospital (Rome, Italy) from September 2010 to March 2011, with the aim to identify the most probable sequences of organs failures in the first week after the ICU admission. Approximately 56% of patients were admitted into the ICU with lung failure and about 27% of patients with heart failure. Results suggest that, given the first organ failure at the ICU admission, a sequence of organ failures can be predicted with a certain degree of probability. Sequences involving heart, lung, hematologic system and liver turned out to be the more likely to occur, with slightly different probabilities depending on the day of the week they occur. DBNs could be successfully applied for modeling temporal systems in critical care domain. Capability to predict sequences of likely organ failures makes DBNs a promising prognostic tool, intended to help physicians in undertaking therapeutic decisions in a patient-tailored approach.},\n bibtype = {article},\n author = {Sandri, Micol and Berchialla, Paola and Baldi, Ileana and Gregori, Dario and De Blasi, Roberto Alberto},\n doi = {10.1016/j.jbi.2013.12.008},\n journal = {Journal of biomedical informatics}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Multi Organ Dysfunction Syndrome (MODS) represents a continuum of physiologic derangements and is the major cause of death in the Intensive Care Unit (ICU). Scoring systems for organ failure have become an integral part of critical care practice and play an important role in ICU-based research by tracking disease progression and facilitating patient stratification based on evaluation of illness severity during ICU stay. In this study a Dynamic Bayesian Network (DBN) was applied to model SOFA severity score changes in 79 adult critically ill patients consecutively admitted to the general ICU of the Sant'Andrea University hospital (Rome, Italy) from September 2010 to March 2011, with the aim to identify the most probable sequences of organs failures in the first week after the ICU admission. Approximately 56% of patients were admitted into the ICU with lung failure and about 27% of patients with heart failure. Results suggest that, given the first organ failure at the ICU admission, a sequence of organ failures can be predicted with a certain degree of probability. Sequences involving heart, lung, hematologic system and liver turned out to be the more likely to occur, with slightly different probabilities depending on the day of the week they occur. DBNs could be successfully applied for modeling temporal systems in critical care domain. Capability to predict sequences of likely organ failures makes DBNs a promising prognostic tool, intended to help physicians in undertaking therapeutic decisions in a patient-tailored approach.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"seixas-zadrozny-laks-conci-muchaluatsaade-abayesiannetworkdecisionmodelforsupportingthediagnosisofdementiaalzheimersdiseaseandmildcognitiveimpairment-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482514000961\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'seixas-zadrozny-laks-conci-muchaluatsaade-abayesiannetworkdecisionmodelforsupportingthediagnosisofdementiaalzheimersdiseaseandmildcognitiveimpairment-2014', 'http://www.sciencedirect.com/science/article/pii/S0010482514000961', event);\">\n    A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Seixas,  F., L.; Zadrozny,  B.; Laks,  J.; Conci,  A.;  and Muchaluat Saade,  D., C.\n</span>\n\n  \n\n</span>\n\n  <i>Computers in biology and medicine</i>, 51: 140-58. 8 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482514000961\"\n     onclick=\"log_download('seixas-zadrozny-laks-conci-muchaluatsaade-abayesiannetworkdecisionmodelforsupportingthediagnosisofdementiaalzheimersdiseaseandmildcognitiveimpairment-2014', 'http://www.sciencedirect.com/science/article/pii/S0010482514000961', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.compbiomed.2014.04.010\"\n     onclick=\"log_download('seixas-zadrozny-laks-conci-muchaluatsaade-abayesiannetworkdecisionmodelforsupportingthediagnosisofdementiaalzheimersdiseaseandmildcognitiveimpairment-2014', 'http://doi.org/10.1016/j.compbiomed.2014.04.010', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/seixas-zadrozny-laks-conci-muchaluatsaade-abayesiannetworkdecisionmodelforsupportingthediagnosisofdementiaalzheimersdiseaseandmildcognitiveimpairment-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('seixas-zadrozny-laks-conci-muchaluatsaade-abayesiannetworkdecisionmodelforsupportingthediagnosisofdementiaalzheimersdiseaseandmildcognitiveimpairment-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment.},\n type = {article},\n year = {2014},\n keywords = {Alzheimer Disease,Alzheimer Disease: classification,Alzheimer Disease: diagnosis,Alzheimer Disease: physiopathology,Bayes Theorem,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Mental Disorders,Mental Disorders: classification,Mental Disorders: diagnosis,Mental Disorders: physiopathology,Models, Biological},\n pages = {140-58},\n volume = {51},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482514000961},\n month = {8},\n id = {098541ed-8785-3bdd-9a21-5db89d4f8b85},\n created = {2015-04-12T20:17:33.000Z},\n accessed = {2015-04-08},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.},\n bibtype = {article},\n author = {Seixas, Flávio Luiz and Zadrozny, Bianca and Laks, Jerson and Conci, Aura and Muchaluat Saade, Débora Christina},\n doi = {10.1016/j.compbiomed.2014.04.010},\n journal = {Computers in biology and medicine}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"vanderheijden-velikova-lucas-learningbayesiannetworksforclinicaltimeseriesanalysis-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046413001986\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'vanderheijden-velikova-lucas-learningbayesiannetworksforclinicaltimeseriesanalysis-2014', 'http://www.sciencedirect.com/science/article/pii/S1532046413001986', event);\">\n    Learning Bayesian networks for clinical time series analysis.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  van der Heijden,  M.; Velikova,  M.;  and Lucas,  P., J., F.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of biomedical informatics</i>, 48: 94-105. 4 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046413001986\"\n     onclick=\"log_download('vanderheijden-velikova-lucas-learningbayesiannetworksforclinicaltimeseriesanalysis-2014', 'http://www.sciencedirect.com/science/article/pii/S1532046413001986', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Learning Bayesian networks for clinical time series analysis. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jbi.2013.12.007\"\n     onclick=\"log_download('vanderheijden-velikova-lucas-learningbayesiannetworksforclinicaltimeseriesanalysis-2014', 'http://doi.org/10.1016/j.jbi.2013.12.007', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vanderheijden-velikova-lucas-learningbayesiannetworksforclinicaltimeseriesanalysis-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('vanderheijden-velikova-lucas-learningbayesiannetworksforclinicaltimeseriesanalysis-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Learning Bayesian networks for clinical time series analysis.},\n type = {article},\n year = {2014},\n keywords = {Aged,Algorithms,Area Under Curve,Artificial Intelligence,Bayes Theorem,Computer Simulation,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Female,Humans,Lung,Lung: physiology,Male,Middle Aged,Monitoring, Ambulatory,Monitoring, Ambulatory: methods,Probability,Pulmonary Disease, Chronic Obstructive,Pulmonary Disease, Chronic Obstructive: therapy,Signal Processing, Computer-Assisted,Time Factors},\n pages = {94-105},\n volume = {48},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046413001986},\n month = {4},\n id = {5a113d64-7b66-3d77-88c5-32fbe2e25e03},\n created = {2015-04-12T20:17:34.000Z},\n accessed = {2015-04-07},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {INTRODUCTION: Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status.\n\nMETHODS: Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set.\n\nRESULTS: The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.},\n bibtype = {article},\n author = {van der Heijden, Maarten and Velikova, Marina and Lucas, Peter J F},\n doi = {10.1016/j.jbi.2013.12.007},\n journal = {Journal of biomedical informatics}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  INTRODUCTION: Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status.\n\nMETHODS: Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set.\n\nRESULTS: The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wang-makond-wang-modelingandpredictingtheoccurrenceofbrainmetastasisfromlungcancerbybayesiannetworkacasestudyoftaiwan-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S001048251400033X\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wang-makond-wang-modelingandpredictingtheoccurrenceofbrainmetastasisfromlungcancerbybayesiannetworkacasestudyoftaiwan-2014', 'http://www.sciencedirect.com/science/article/pii/S001048251400033X', event);\">\n    Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: a case study of Taiwan.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang,  K.; Makond,  B.;  and Wang,  K.\n</span>\n\n  \n\n</span>\n\n  <i>Computers in biology and medicine</i>, 47: 147-60. 4 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S001048251400033X\"\n     onclick=\"log_download('wang-makond-wang-modelingandpredictingtheoccurrenceofbrainmetastasisfromlungcancerbybayesiannetworkacasestudyoftaiwan-2014', 'http://www.sciencedirect.com/science/article/pii/S001048251400033X', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: a case study of Taiwan. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.compbiomed.2014.02.002\"\n     onclick=\"log_download('wang-makond-wang-modelingandpredictingtheoccurrenceofbrainmetastasisfromlungcancerbybayesiannetworkacasestudyoftaiwan-2014', 'http://doi.org/10.1016/j.compbiomed.2014.02.002', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-makond-wang-modelingandpredictingtheoccurrenceofbrainmetastasisfromlungcancerbybayesiannetworkacasestudyoftaiwan-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-makond-wang-modelingandpredictingtheoccurrenceofbrainmetastasisfromlungcancerbybayesiannetworkacasestudyoftaiwan-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: a case study of Taiwan.},\n type = {article},\n year = {2014},\n keywords = {Aged,Algorithms,Bayes Theorem,Brain Neoplasms,Brain Neoplasms: epidemiology,Brain Neoplasms: secondary,Computational Biology,Computational Biology: methods,Female,Humans,Lung Neoplasms,Lung Neoplasms: epidemiology,Lung Neoplasms: pathology,Male,Middle Aged,Models, Statistical,Sensitivity and Specificity,Taiwan,Taiwan: epidemiology},\n pages = {147-60},\n volume = {47},\n websites = {http://www.sciencedirect.com/science/article/pii/S001048251400033X},\n month = {4},\n id = {c853e323-681e-3ae4-80c6-3b543722b7f4},\n created = {2015-04-12T20:17:34.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The Bayesian network (BN) is a promising method for modeling cancer metastasis under uncertainty. BN is graphically represented using bioinformatics variables and can be used to support an informative medical decision/observation by using probabilistic reasoning. In this study, we propose such a BN to describe and predict the occurrence of brain metastasis from lung cancer. A nationwide database containing more than 50,000 cases of cancer patients from 1996 to 2010 in Taiwan was used in this study. The BN topology for studying brain metastasis from lung cancer was rigorously examined by domain experts/doctors. We used three statistical measures, namely, the accuracy, sensitivity, and specificity, to evaluate the performances of the proposed BN model and to compare it with three competitive approaches, namely, naive Bayes (NB), logistic regression (LR) and support vector machine (SVM). Experimental results show that no significant differences are observed in accuracy or specificity among the four models, while the proposed BN outperforms the others in terms of sampled average sensitivity. Moreover the proposed BN has advantages compared with the other approaches in interpreting how brain metastasis develops from lung cancer. It is shown to be easily understood by physicians, to be efficient in modeling non-linear situations, capable of solving stochastic medical problems, and handling situations wherein information are missing in the context of the occurrence of brain metastasis from lung cancer.},\n bibtype = {article},\n author = {Wang, Kung-Jeng and Makond, Bunjira and Wang, Kung-Min},\n doi = {10.1016/j.compbiomed.2014.02.002},\n journal = {Computers in biology and medicine}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The Bayesian network (BN) is a promising method for modeling cancer metastasis under uncertainty. BN is graphically represented using bioinformatics variables and can be used to support an informative medical decision/observation by using probabilistic reasoning. In this study, we propose such a BN to describe and predict the occurrence of brain metastasis from lung cancer. A nationwide database containing more than 50,000 cases of cancer patients from 1996 to 2010 in Taiwan was used in this study. The BN topology for studying brain metastasis from lung cancer was rigorously examined by domain experts/doctors. We used three statistical measures, namely, the accuracy, sensitivity, and specificity, to evaluate the performances of the proposed BN model and to compare it with three competitive approaches, namely, naive Bayes (NB), logistic regression (LR) and support vector machine (SVM). Experimental results show that no significant differences are observed in accuracy or specificity among the four models, while the proposed BN outperforms the others in terms of sampled average sensitivity. Moreover the proposed BN has advantages compared with the other approaches in interpreting how brain metastasis develops from lung cancer. It is shown to be easily understood by physicians, to be efficient in modeling non-linear situations, capable of solving stochastic medical problems, and handling situations wherein information are missing in the context of the occurrence of brain metastasis from lung cancer.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046413001640\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014', 'http://www.sciencedirect.com/science/article/pii/S1532046413001640', event);\">\n    Not just data: a method for improving prediction with knowledge.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yet,  B.; Perkins,  Z.; Fenton,  N.; Tai,  N.;  and Marsh,  W.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of biomedical informatics</i>, 48: 28-37. 4 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046413001640\"\n     onclick=\"log_download('yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014', 'http://www.sciencedirect.com/science/article/pii/S1532046413001640', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Not just data: a method for improving prediction with knowledge. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jbi.2013.10.012\"\n     onclick=\"log_download('yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014', 'http://doi.org/10.1016/j.jbi.2013.10.012', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Not just data: a method for improving prediction with knowledge.},\n type = {article},\n year = {2014},\n keywords = {Algorithms,Bayes Theorem,Blood Coagulation,Blood Coagulation Disorders,Blood Coagulation Disorders: therapy,Cluster Analysis,Decision Making,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Emergency Medical Services,Emergency Medical Services: organization &amp; adminis,Humans,Medical Errors,Medical Errors: prevention &amp; control,Medical Informatics,Medical Informatics: methods,Medical Informatics: trends,Risk Assessment,Sensitivity and Specificity},\n pages = {28-37},\n volume = {48},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046413001640},\n month = {4},\n id = {1d43401a-3c97-343a-b1b0-d9e0f0ab9781},\n created = {2015-04-12T20:17:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as &#x27;latent&#x27; variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.},\n bibtype = {article},\n author = {Yet, Barbaros and Perkins, Zane and Fenton, Norman and Tai, Nigel and Marsh, William},\n doi = {10.1016/j.jbi.2013.10.012},\n journal = {Journal of biomedical informatics}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://darchive.mblwhoilibrary.org/handle/1912/7233\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014', 'https://darchive.mblwhoilibrary.org/handle/1912/7233', event);\">\n    A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gieder,  K., D.; Karpanty,  S., M.; Fraser,  J., D.; Catlin,  D., H.; Gutierrez,  B., T.; Plant,  N., G.; Turecek,  A., M.;  and Robert Thieler,  E.\n</span>\n\n  \n\n</span>\n\n  <i>Ecological Modelling</i>, 276: 38-50. 3 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://darchive.mblwhoilibrary.org/handle/1912/7233\"\n     onclick=\"log_download('gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014', 'https://darchive.mblwhoilibrary.org/handle/1912/7233', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ecolmodel.2014.01.005\"\n     onclick=\"log_download('gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014', 'http://doi.org/10.1016/j.ecolmodel.2014.01.005', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gieder-karpanty-fraser-catlin-gutierrez-plant-turecek-robertthieler-abayesiannetworkapproachtopredictingnestpresenceofthefederallythreatenedpipingplovercharadriusmelodususingbarrierislandfeatures-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features},\n type = {article},\n year = {2014},\n keywords = {Bayesian network,Development,Habitat,Piping plover,Sea-level rise,Shorebird},\n pages = {38-50},\n volume = {276},\n websites = {https://darchive.mblwhoilibrary.org/handle/1912/7233},\n month = {3},\n publisher = {Elsevier},\n day = {31},\n id = {18bdf9c6-a58f-3f8a-8215-c2758447ea1d},\n created = {2015-05-07T19:12:09.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n language = {en_US},\n private_publication = {false},\n abstract = {© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Modelling 276 (2014): 38–50, doi:10.1016/j.ecolmodel.2014.01.005.},\n bibtype = {article},\n author = {Gieder, Katherina D. and Karpanty, Sarah M. and Fraser, James D. and Catlin, Daniel H. and Gutierrez, Benjamin T. and Plant, Nathaniel G. and Turecek, Aaron M. and Robert Thieler, E.},\n doi = {10.1016/j.ecolmodel.2014.01.005},\n journal = {Ecological Modelling}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  © The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Modelling 276 (2014): 38–50, doi:10.1016/j.ecolmodel.2014.01.005.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2013\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2013')\"\n      onkeypress=\"toggleGroup('group_2013')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2013</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"corani-magli-giusti-gianaroli-gambardella-abayesiannetworkmodelforpredictingpregnancyafterinvitrofertilization-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482513002187\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'corani-magli-giusti-gianaroli-gambardella-abayesiannetworkmodelforpredictingpregnancyafterinvitrofertilization-2013', 'http://www.sciencedirect.com/science/article/pii/S0010482513002187', event);\">\n    A Bayesian network model for predicting pregnancy after in vitro fertilization.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Corani,  G.; Magli,  C.; Giusti,  A.; Gianaroli,  L.;  and Gambardella,  L., M.\n</span>\n\n  \n\n</span>\n\n  <i>Computers in biology and medicine</i>, 43(11): 1783-92. 11 2013.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482513002187\"\n     onclick=\"log_download('corani-magli-giusti-gianaroli-gambardella-abayesiannetworkmodelforpredictingpregnancyafterinvitrofertilization-2013', 'http://www.sciencedirect.com/science/article/pii/S0010482513002187', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Bayesian network model for predicting pregnancy after in vitro fertilization. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.compbiomed.2013.07.035\"\n     onclick=\"log_download('corani-magli-giusti-gianaroli-gambardella-abayesiannetworkmodelforpredictingpregnancyafterinvitrofertilization-2013', 'http://doi.org/10.1016/j.compbiomed.2013.07.035', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/corani-magli-giusti-gianaroli-gambardella-abayesiannetworkmodelforpredictingpregnancyafterinvitrofertilization-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('corani-magli-giusti-gianaroli-gambardella-abayesiannetworkmodelforpredictingpregnancyafterinvitrofertilization-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Bayesian network model for predicting pregnancy after in vitro fertilization.},\n type = {article},\n year = {2013},\n keywords = {Adult,Algorithms,Area Under Curve,Bayes Theorem,Computer Simulation,Embryo Transfer,Embryo Transfer: statistics &amp; numerical data,Embryo, Mammalian,Female,Fertilization in Vitro,Fertilization in Vitro: statistics &amp; numerical dat,Humans,Pregnancy,Pregnancy: statistics &amp; numerical data},\n pages = {1783-92},\n volume = {43},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482513002187},\n month = {11},\n id = {ea7467fd-0819-3352-ac67-6489e0f12820},\n created = {2015-04-12T18:44:13.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We present a Bayesian network model for predicting the outcome of in vitro fertilization (IVF). The problem is characterized by a particular missingness process; we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. We present results with generated data and the analysis of a real data set. Moreover, we assess by means of a simulation study the effectiveness of the model in supporting the selection of the embryos to be transferred.},\n bibtype = {article},\n author = {Corani, G and Magli, C and Giusti, A and Gianaroli, L and Gambardella, L M},\n doi = {10.1016/j.compbiomed.2013.07.035},\n journal = {Computers in biology and medicine},\n number = {11}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We present a Bayesian network model for predicting the outcome of in vitro fertilization (IVF). The problem is characterized by a particular missingness process; we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. We present results with generated data and the analysis of a real data set. Moreover, we assess by means of a simulation study the effectiveness of the model in supporting the selection of the embryos to be transferred.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sohn-lee-bayesiannetworkanalysisforthedynamicpredictionofearlystageentrepreneurialactivityindex-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0957417413000122\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sohn-lee-bayesiannetworkanalysisforthedynamicpredictionofearlystageentrepreneurialactivityindex-2013', 'http://www.sciencedirect.com/science/article/pii/S0957417413000122', event);\">\n    Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sohn,  S., Y.;  and Lee,  A., S.\n</span>\n\n  \n\n</span>\n\n  <i>Expert Systems with Applications</i>, 40(10): 4003-4009. 8 2013.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0957417413000122\"\n     onclick=\"log_download('sohn-lee-bayesiannetworkanalysisforthedynamicpredictionofearlystageentrepreneurialactivityindex-2013', 'http://www.sciencedirect.com/science/article/pii/S0957417413000122', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.eswa.2013.01.009\"\n     onclick=\"log_download('sohn-lee-bayesiannetworkanalysisforthedynamicpredictionofearlystageentrepreneurialactivityindex-2013', 'http://doi.org/10.1016/j.eswa.2013.01.009', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sohn-lee-bayesiannetworkanalysisforthedynamicpredictionofearlystageentrepreneurialactivityindex-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sohn-lee-bayesiannetworkanalysisforthedynamicpredictionofearlystageentrepreneurialactivityindex-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index},\n type = {article},\n year = {2013},\n keywords = {Bayesian network,Early stage entrepreneurial activity Index,Forecasting,GEM},\n pages = {4003-4009},\n volume = {40},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417413000122},\n month = {8},\n id = {4f4aa7b1-b0f6-3c9a-a260-cb8d1af6a8a8},\n created = {2015-04-16T00:39:20.000Z},\n accessed = {2015-02-14},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Entrepreneurship plays a critical role for the development and well-being of society. Illustration of its dynamic relationship with entrepreneurial attitudes and aspirations can provide a guideline for the cause of such activities. However, a time-lagged causal relationship among these concepts has not yet been established. In this study, we examine a dynamic relationship among early stage entrepreneurial attitudes, activities, and aspirations using Bayesian network (BN) analysis. In addition, we propose an early stage entrepreneurial activity index that can predict the percentage of both nascent entrepreneur and new business owner using the variables related to entrepreneurial attitudes of the previous year. This index, in turn, can be used to predict various aspects of entrepreneurial aspiration of the following year. The proposed index turns out to have very high prediction accuracy and is expected to provide effective policies to boost future entrepreneurial activity and aspiration.},\n bibtype = {article},\n author = {Sohn, So Young and Lee, Ann Sung},\n doi = {10.1016/j.eswa.2013.01.009},\n journal = {Expert Systems with Applications},\n number = {10}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Entrepreneurship plays a critical role for the development and well-being of society. Illustration of its dynamic relationship with entrepreneurial attitudes and aspirations can provide a guideline for the cause of such activities. However, a time-lagged causal relationship among these concepts has not yet been established. In this study, we examine a dynamic relationship among early stage entrepreneurial attitudes, activities, and aspirations using Bayesian network (BN) analysis. In addition, we propose an early stage entrepreneurial activity index that can predict the percentage of both nascent entrepreneur and new business owner using the variables related to entrepreneurial attitudes of the previous year. This index, in turn, can be used to predict various aspects of entrepreneurial aspiration of the following year. The proposed index turns out to have very high prediction accuracy and is expected to provide effective policies to boost future entrepreneurial activity and aspiration.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2012\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2012')\"\n      onkeypress=\"toggleGroup('group_2012')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2012</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"masoudi-tokhmechi-ansarijafari-zamanzadeh-sherkati-applicationofbayesianindeterminingproductivezonesbywelllogdatainoilwells-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0920410512001702\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'masoudi-tokhmechi-ansarijafari-zamanzadeh-sherkati-applicationofbayesianindeterminingproductivezonesbywelllogdatainoilwells-2012', 'http://www.sciencedirect.com/science/article/pii/S0920410512001702', event);\">\n    Application of Bayesian in determining productive zones by well log data in oil wells.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Masoudi,  P.; Tokhmechi,  B.; Ansari Jafari,  M.; Zamanzadeh,  S., M.;  and Sherkati,  S.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Petroleum Science and Engineering</i>, 94-95: 47-54. 9 2012.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0920410512001702\"\n     onclick=\"log_download('masoudi-tokhmechi-ansarijafari-zamanzadeh-sherkati-applicationofbayesianindeterminingproductivezonesbywelllogdatainoilwells-2012', 'http://www.sciencedirect.com/science/article/pii/S0920410512001702', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Application of Bayesian in determining productive zones by well log data in oil wells [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.petrol.2012.06.028\"\n     onclick=\"log_download('masoudi-tokhmechi-ansarijafari-zamanzadeh-sherkati-applicationofbayesianindeterminingproductivezonesbywelllogdatainoilwells-2012', 'http://doi.org/10.1016/j.petrol.2012.06.028', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/masoudi-tokhmechi-ansarijafari-zamanzadeh-sherkati-applicationofbayesianindeterminingproductivezonesbywelllogdatainoilwells-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('masoudi-tokhmechi-ansarijafari-zamanzadeh-sherkati-applicationofbayesianindeterminingproductivezonesbywelllogdatainoilwells-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Application of Bayesian in determining productive zones by well log data in oil wells},\n type = {article},\n year = {2012},\n keywords = {Bayesian Network,net pay,petrophysics,productive zone,well test},\n pages = {47-54},\n volume = {94-95},\n websites = {http://www.sciencedirect.com/science/article/pii/S0920410512001702},\n month = {9},\n id = {5e0764ec-d115-302e-b597-ef9875329209},\n created = {2015-04-12T18:30:34.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Exploration specialists conventionally utilize a cut-off-based method to find productive zones inside the oil wells. Using conventional method, pay zones are separated crisply from non-pay zones by applying cut-off values on some petrophysical features. In this paper, a Bayesian technique is developed to find productive zones (net pays), and Bayesian Network is used to select the most appropriate input features for this newly developed method. So, two Bayesian methods were developed: the first one with conventional pay determination inputs (shale percent, porosity and water saturation), the other with two inputs, selected by Bayesian Network (porosity and water saturation). Two developed Bayesian methods are applied on well log dataset of two wells: one well is dedicated for training and testing Bayesian methods, the other for checking generalization ability of the proposed methods. Outputs of two presented methods were compared with the results of conventional cut-off-based method and production test results (i.e. a direct procedure to check validation of proposed methods). Results show that the most prominent advantage of developed Bayesian method is determination of net pays fuzzily with no need to identify cut-offs, in addition to higher precision of classification: nearly 30% improvement in precision of determining net pays of first well (training well), and about 50% improvement in precision of determining productive zones through the generalizing well.},\n bibtype = {article},\n author = {Masoudi, Pedram and Tokhmechi, Behzad and Ansari Jafari, Majid and Zamanzadeh, Seyed Mohammad and Sherkati, Shahram},\n doi = {10.1016/j.petrol.2012.06.028},\n journal = {Journal of Petroleum Science and Engineering}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Exploration specialists conventionally utilize a cut-off-based method to find productive zones inside the oil wells. Using conventional method, pay zones are separated crisply from non-pay zones by applying cut-off values on some petrophysical features. In this paper, a Bayesian technique is developed to find productive zones (net pays), and Bayesian Network is used to select the most appropriate input features for this newly developed method. So, two Bayesian methods were developed: the first one with conventional pay determination inputs (shale percent, porosity and water saturation), the other with two inputs, selected by Bayesian Network (porosity and water saturation). Two developed Bayesian methods are applied on well log dataset of two wells: one well is dedicated for training and testing Bayesian methods, the other for checking generalization ability of the proposed methods. Outputs of two presented methods were compared with the results of conventional cut-off-based method and production test results (i.e. a direct procedure to check validation of proposed methods). Results show that the most prominent advantage of developed Bayesian method is determination of net pays fuzzily with no need to identify cut-offs, in addition to higher precision of classification: nearly 30% improvement in precision of determining net pays of first well (training well), and about 50% improvement in precision of determining productive zones through the generalizing well.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"borchani-bielza-martnezmartn-larraaga-markovblanketbasedapproachforlearningmultidimensionalbayesiannetworkclassifiersanapplicationtopredicttheeuropeanqualityoflife5dimensionseq5dfromthe39itemparkinsonsdiseasequestionnairepdq39-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046412001074\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'borchani-bielza-martnezmartn-larraaga-markovblanketbasedapproachforlearningmultidimensionalbayesiannetworkclassifiersanapplicationtopredicttheeuropeanqualityoflife5dimensionseq5dfromthe39itemparkinsonsdiseasequestionnairepdq39-2012', 'http://www.sciencedirect.com/science/article/pii/S1532046412001074', event);\">\n    Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Borchani,  H.; Bielza,  C.; Martı Nez-Martı N,  P.;  and Larrañaga,  P.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of biomedical informatics</i>, 45(6): 1175-84. 12 2012.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046412001074\"\n     onclick=\"log_download('borchani-bielza-martnezmartn-larraaga-markovblanketbasedapproachforlearningmultidimensionalbayesiannetworkclassifiersanapplicationtopredicttheeuropeanqualityoflife5dimensionseq5dfromthe39itemparkinsonsdiseasequestionnairepdq39-2012', 'http://www.sciencedirect.com/science/article/pii/S1532046412001074', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson&#x27;s Disease Questionnaire (PDQ-39). [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jbi.2012.07.010\"\n     onclick=\"log_download('borchani-bielza-martnezmartn-larraaga-markovblanketbasedapproachforlearningmultidimensionalbayesiannetworkclassifiersanapplicationtopredicttheeuropeanqualityoflife5dimensionseq5dfromthe39itemparkinsonsdiseasequestionnairepdq39-2012', 'http://doi.org/10.1016/j.jbi.2012.07.010', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/borchani-bielza-martnezmartn-larraaga-markovblanketbasedapproachforlearningmultidimensionalbayesiannetworkclassifiersanapplicationtopredicttheeuropeanqualityoflife5dimensionseq5dfromthe39itemparkinsonsdiseasequestionnairepdq39-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('borchani-bielza-martnezmartn-larraaga-markovblanketbasedapproachforlearningmultidimensionalbayesiannetworkclassifiersanapplicationtopredicttheeuropeanqualityoflife5dimensionseq5dfromthe39itemparkinsonsdiseasequestionnairepdq39-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson&#x27;s Disease Questionnaire (PDQ-39).},\n type = {article},\n year = {2012},\n keywords = {Bayes Theorem,Health Status,Humans,Markov Chains,Parkinson Disease,Quality of Life,Questionnaires},\n pages = {1175-84},\n volume = {45},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046412001074},\n month = {12},\n id = {7c918dcd-9adb-3846-a93a-08f76992fe8d},\n created = {2015-04-12T18:44:13.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson&#x27;s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson&#x27;s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson&#x27;s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.},\n bibtype = {article},\n author = {Borchani, Hanen and Bielza, Concha and Martı Nez-Martı N, Pablo and Larrañaga, Pedro},\n doi = {10.1016/j.jbi.2012.07.010},\n journal = {Journal of biomedical informatics},\n number = {6}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2011\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2011')\"\n      onkeypress=\"toggleGroup('group_2011')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2011</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"dlamini-adataminingapproachtopredictivevegetationmappingusingprobabilisticgraphicalmodels-2011\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1574954111000045\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'dlamini-adataminingapproachtopredictivevegetationmappingusingprobabilisticgraphicalmodels-2011', 'http://www.sciencedirect.com/science/article/pii/S1574954111000045', event);\">\n    A data mining approach to predictive vegetation mapping using probabilistic graphical models.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Dlamini,  W., M.\n</span>\n\n  \n\n</span>\n\n  <i>Ecological Informatics</i>, 6(2): 111-124. 3 2011.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1574954111000045\"\n     onclick=\"log_download('dlamini-adataminingapproachtopredictivevegetationmappingusingprobabilisticgraphicalmodels-2011', 'http://www.sciencedirect.com/science/article/pii/S1574954111000045', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A data mining approach to predictive vegetation mapping using probabilistic graphical models [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ecoinf.2010.12.005\"\n     onclick=\"log_download('dlamini-adataminingapproachtopredictivevegetationmappingusingprobabilisticgraphicalmodels-2011', 'http://doi.org/10.1016/j.ecoinf.2010.12.005', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dlamini-adataminingapproachtopredictivevegetationmappingusingprobabilisticgraphicalmodels-2011\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dlamini-adataminingapproachtopredictivevegetationmappingusingprobabilisticgraphicalmodels-2011')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A data mining approach to predictive vegetation mapping using probabilistic graphical models},\n type = {article},\n year = {2011},\n keywords = {Bayesian network,Data mining,Expectation-maximization,Graphical model,Predictive vegetation mapping},\n pages = {111-124},\n volume = {6},\n websites = {http://www.sciencedirect.com/science/article/pii/S1574954111000045},\n month = {3},\n id = {4b6617c5-cc10-33bd-a91f-6af94ae4a28f},\n created = {2015-04-12T18:44:13.000Z},\n accessed = {2015-02-16},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper develops a novel method to model and predict the spatial distribution of vegetation types in Swaziland using physiographic and bioclimatic variables. The method uses a data mining approach implemented within a probabilistic graphical model to match two observed hierarchical levels of vegetation. The classification uses Bayesian networks (BN) and the parameterization is based on the expectation-maximization (EM) algorithm. The model is tested on a random sample of mapped vegetation types in Swaziland and allowed for the identification of the key environmental variables that are most important for capturing the vegetation spatial distribution. We show that while elevation and geology are the most important variables explaining the spatial distribution patterns of vegetation for both models, the influence of the climatic and other variables on the vegetation at the two levels differ. The overall distribution of the predicted vegetation classes was very similar to their distribution on the observed vegetation map. Overall the error rate was found to be 9.35% for a model of 16 vegetation classes and 4.9% for the one with 5 classes, indicating the excellent classification accuracy of the approach despite the complex landscape of the study area. Possible sources of error and some limitations are discussed and conclusions are drawn including suggestions for further investigation.},\n bibtype = {article},\n author = {Dlamini, Wisdom M.},\n doi = {10.1016/j.ecoinf.2010.12.005},\n journal = {Ecological Informatics},\n number = {2}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper develops a novel method to model and predict the spatial distribution of vegetation types in Swaziland using physiographic and bioclimatic variables. The method uses a data mining approach implemented within a probabilistic graphical model to match two observed hierarchical levels of vegetation. The classification uses Bayesian networks (BN) and the parameterization is based on the expectation-maximization (EM) algorithm. The model is tested on a random sample of mapped vegetation types in Swaziland and allowed for the identification of the key environmental variables that are most important for capturing the vegetation spatial distribution. We show that while elevation and geology are the most important variables explaining the spatial distribution patterns of vegetation for both models, the influence of the climatic and other variables on the vegetation at the two levels differ. The overall distribution of the predicted vegetation classes was very similar to their distribution on the observed vegetation map. Overall the error rate was found to be 9.35% for a model of 16 vegetation classes and 4.9% for the one with 5 classes, indicating the excellent classification accuracy of the approach despite the complex landscape of the study area. Possible sources of error and some limitations are discussed and conclusions are drawn including suggestions for further investigation.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lewis-brlisauer-gunn-structurediscoveryinbayesiannetworksananalyticaltoolforanalysingcomplexanimalhealthdata-2011\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0167587711000341\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lewis-brlisauer-gunn-structurediscoveryinbayesiannetworksananalyticaltoolforanalysingcomplexanimalhealthdata-2011', 'http://www.sciencedirect.com/science/article/pii/S0167587711000341', event);\">\n    Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lewis,  F.; Brülisauer,  F.;  and Gunn,  G.\n</span>\n\n  \n\n</span>\n\n  <i>Preventive Veterinary Medicine</i>, 100(2): 109-115. 6 2011.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0167587711000341\"\n     onclick=\"log_download('lewis-brlisauer-gunn-structurediscoveryinbayesiannetworksananalyticaltoolforanalysingcomplexanimalhealthdata-2011', 'http://www.sciencedirect.com/science/article/pii/S0167587711000341', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.prevetmed.2011.02.003\"\n     onclick=\"log_download('lewis-brlisauer-gunn-structurediscoveryinbayesiannetworksananalyticaltoolforanalysingcomplexanimalhealthdata-2011', 'http://doi.org/10.1016/j.prevetmed.2011.02.003', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lewis-brlisauer-gunn-structurediscoveryinbayesiannetworksananalyticaltoolforanalysingcomplexanimalhealthdata-2011\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lewis-brlisauer-gunn-structurediscoveryinbayesiannetworksananalyticaltoolforanalysingcomplexanimalhealthdata-2011')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data},\n type = {article},\n year = {2011},\n keywords = {Knowledge Discovery},\n pages = {109-115},\n volume = {100},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587711000341},\n month = {6},\n id = {d7020f3c-f6cc-3407-b484-b635b8d5047b},\n created = {2015-04-12T19:14:40.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Analysing animal health data can be a complex task as the health status of individuals or groups of animals, might depend on many inter-related variables. The objective is to differentiate variables that are directly associated with health status and therefore promising targets for intervention, from variables that are indirectly associated with health status and can therefore at best only affect this indirectly through association with other variables. Bayesian network (BN) modelling is a machine learning technique for empirically identifying associations in complex and high dimensional data, so-called “structure discovery”. An introduction to structure discovery using BN modelling is presented, comprising the key assumptions required by the methodology, along with a discussion of advantages and limitations. To demonstrate the various steps required to apply BN structure discovery to animal health data, illustrative analyses of data collected during a previously published study concerned with exposure to bovine viral diarrhoea virus in beef cow-calf herds in Scotland are presented.},\n bibtype = {article},\n author = {Lewis, F.I. and Brülisauer, F. and Gunn, G.J.},\n doi = {10.1016/j.prevetmed.2011.02.003},\n journal = {Preventive Veterinary Medicine},\n number = {2}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Analysing animal health data can be a complex task as the health status of individuals or groups of animals, might depend on many inter-related variables. The objective is to differentiate variables that are directly associated with health status and therefore promising targets for intervention, from variables that are indirectly associated with health status and can therefore at best only affect this indirectly through association with other variables. Bayesian network (BN) modelling is a machine learning technique for empirically identifying associations in complex and high dimensional data, so-called “structure discovery”. An introduction to structure discovery using BN modelling is presented, comprising the key assumptions required by the methodology, along with a discussion of advantages and limitations. To demonstrate the various steps required to apply BN structure discovery to animal health data, illustrative analyses of data collected during a previously published study concerned with exposure to bovine viral diarrhoea virus in beef cow-calf herds in Scotland are presented.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2010\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2010')\"\n      onkeypress=\"toggleGroup('group_2010')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2010</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"yang-lin-bhattacharya-adriverfatiguerecognitionmodelbasedoninformationfusionanddynamicbayesiannetwork-2010\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0020025510000253\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'yang-lin-bhattacharya-adriverfatiguerecognitionmodelbasedoninformationfusionanddynamicbayesiannetwork-2010', 'http://www.sciencedirect.com/science/article/pii/S0020025510000253', event);\">\n    A driver fatigue recognition model based on information fusion and dynamic Bayesian network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yang,  G.; Lin,  Y.;  and Bhattacharya,  P.\n</span>\n\n  \n\n</span>\n\n  <i>Information Sciences</i>, 180(10): 1942-1954. 5 2010.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0020025510000253\"\n     onclick=\"log_download('yang-lin-bhattacharya-adriverfatiguerecognitionmodelbasedoninformationfusionanddynamicbayesiannetwork-2010', 'http://www.sciencedirect.com/science/article/pii/S0020025510000253', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A driver fatigue recognition model based on information fusion and dynamic Bayesian network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ins.2010.01.011\"\n     onclick=\"log_download('yang-lin-bhattacharya-adriverfatiguerecognitionmodelbasedoninformationfusionanddynamicbayesiannetwork-2010', 'http://doi.org/10.1016/j.ins.2010.01.011', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yang-lin-bhattacharya-adriverfatiguerecognitionmodelbasedoninformationfusionanddynamicbayesiannetwork-2010\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yang-lin-bhattacharya-adriverfatiguerecognitionmodelbasedoninformationfusionanddynamicbayesiannetwork-2010')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A driver fatigue recognition model based on information fusion and dynamic Bayesian network},\n type = {article},\n year = {2010},\n keywords = {Contextual features,Driver fatigue recognition,Dynamic Bayesian network,Information fusion,Physiological features},\n pages = {1942-1954},\n volume = {180},\n websites = {http://www.sciencedirect.com/science/article/pii/S0020025510000253},\n month = {5},\n id = {af17e830-7378-3917-9df9-516b00560306},\n created = {2015-04-17T15:30:41.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver.},\n bibtype = {article},\n author = {Yang, Guosheng and Lin, Yingzi and Bhattacharya, Prabir},\n doi = {10.1016/j.ins.2010.01.011},\n journal = {Information Sciences},\n number = {10}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2009\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2009')\"\n      onkeypress=\"toggleGroup('group_2009')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2009</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ettema-stergaard-kristensen-estimationofprobabilityforthepresenceofclawanddigitalskindiseasesbycombiningcowandherdlevelinformationusingabayesiannetwork-2009\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0167587709002323\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ettema-stergaard-kristensen-estimationofprobabilityforthepresenceofclawanddigitalskindiseasesbycombiningcowandherdlevelinformationusingabayesiannetwork-2009', 'http://www.sciencedirect.com/science/article/pii/S0167587709002323', event);\">\n    Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ettema,  J., F.; Østergaard,  S.;  and Kristensen,  A., R.\n</span>\n\n  \n\n</span>\n\n  <i>Preventive veterinary medicine</i>, 92(1-2): 89-98. 11 2009.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0167587709002323\"\n     onclick=\"log_download('ettema-stergaard-kristensen-estimationofprobabilityforthepresenceofclawanddigitalskindiseasesbycombiningcowandherdlevelinformationusingabayesiannetwork-2009', 'http://www.sciencedirect.com/science/article/pii/S0167587709002323', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.prevetmed.2009.08.014\"\n     onclick=\"log_download('ettema-stergaard-kristensen-estimationofprobabilityforthepresenceofclawanddigitalskindiseasesbycombiningcowandherdlevelinformationusingabayesiannetwork-2009', 'http://doi.org/10.1016/j.prevetmed.2009.08.014', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ettema-stergaard-kristensen-estimationofprobabilityforthepresenceofclawanddigitalskindiseasesbycombiningcowandherdlevelinformationusingabayesiannetwork-2009\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ettema-stergaard-kristensen-estimationofprobabilityforthepresenceofclawanddigitalskindiseasesbycombiningcowandherdlevelinformationusingabayesiannetwork-2009')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network.},\n type = {article},\n year = {2009},\n keywords = {Animal Husbandry,Animals,Cattle,Cattle Diseases,Cattle Diseases: epidemiology,Cattle Diseases: prevention &amp; control,Cross-Sectional Studies,Denmark,Denmark: epidemiology,Dermatitis,Dermatitis: epidemiology,Dermatitis: prevention &amp; control,Dermatitis: veterinary,Female,Foot Diseases,Foot Diseases: epidemiology,Foot Diseases: prevention &amp; control,Foot Diseases: veterinary,Hoof and Claw,Lactation,Markov Chains,Monte Carlo Method,Risk Factors,Stochastic Processes},\n pages = {89-98},\n volume = {92},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587709002323},\n month = {11},\n day = {1},\n id = {79bf4cc8-bb0a-3cb3-bea9-905de32abe19},\n created = {2015-04-12T18:44:12.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Cross sectional data on the prevalence of claw and (inter) digital skin diseases on 4854 Holstein Friesian cows in 50 Danish dairy herds was used in a Bayesian network to create herd specific probability distributions for the presence of lameness causing diseases. Parity and lactation stage are identified as risk factors on cow level, for the prevalence of the three lameness causing diseases digital dermatitits, other infectious diseases and claw horn diseases. Four herd level risk factors have been identified; herd size, the use of footbaths, a grazing strategy and total mixed ration. Besides, the data has been used to estimate the random effect of herd on disease prevalence and to find conditional probabilities of cows being lame, given the presence of the three diseases. By considering the 50 herds representative for the Danish population, the estimates for risk factors, conditional probabilities and random herd effects are used to formulate cow-level probability distributions of disease presence in a specific Danish dairy herd. By step-wise inclusion of information on cow- and herd-level risk factors, lameness prevalence and clinical diagnosis of diseases on cows in the herd, the Bayesian network systematically adjusts the probability distributions for disease presence in the specific herd. Information on population-, herd- and cow-level is combined and the uncertainty in inference on disease probability is quantified.},\n bibtype = {article},\n author = {Ettema, Jehan Frans and Østergaard, Søren and Kristensen, Anders Ringgaard},\n doi = {10.1016/j.prevetmed.2009.08.014},\n journal = {Preventive veterinary medicine},\n number = {1-2}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Cross sectional data on the prevalence of claw and (inter) digital skin diseases on 4854 Holstein Friesian cows in 50 Danish dairy herds was used in a Bayesian network to create herd specific probability distributions for the presence of lameness causing diseases. Parity and lactation stage are identified as risk factors on cow level, for the prevalence of the three lameness causing diseases digital dermatitits, other infectious diseases and claw horn diseases. Four herd level risk factors have been identified; herd size, the use of footbaths, a grazing strategy and total mixed ration. Besides, the data has been used to estimate the random effect of herd on disease prevalence and to find conditional probabilities of cows being lame, given the presence of the three diseases. By considering the 50 herds representative for the Danish population, the estimates for risk factors, conditional probabilities and random herd effects are used to formulate cow-level probability distributions of disease presence in a specific Danish dairy herd. By step-wise inclusion of information on cow- and herd-level risk factors, lameness prevalence and clinical diagnosis of diseases on cows in the herd, the Bayesian network systematically adjusts the probability distributions for disease presence in the specific herd. Information on population-, herd- and cow-level is combined and the uncertainty in inference on disease probability is quantified.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"renken-mumby-modellingthedynamicsofcoralreefmacroalgaeusingabayesianbeliefnetworkapproach-2009\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0304380009001525\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'renken-mumby-modellingthedynamicsofcoralreefmacroalgaeusingabayesianbeliefnetworkapproach-2009', 'http://www.sciencedirect.com/science/article/pii/S0304380009001525', event);\">\n    Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Renken,  H.;  and Mumby,  P., J.\n</span>\n\n  \n\n</span>\n\n  <i>Ecological Modelling</i>, 220(9-10): 1305-1314. 5 2009.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0304380009001525\"\n     onclick=\"log_download('renken-mumby-modellingthedynamicsofcoralreefmacroalgaeusingabayesianbeliefnetworkapproach-2009', 'http://www.sciencedirect.com/science/article/pii/S0304380009001525', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ecolmodel.2009.02.022\"\n     onclick=\"log_download('renken-mumby-modellingthedynamicsofcoralreefmacroalgaeusingabayesianbeliefnetworkapproach-2009', 'http://doi.org/10.1016/j.ecolmodel.2009.02.022', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/renken-mumby-modellingthedynamicsofcoralreefmacroalgaeusingabayesianbeliefnetworkapproach-2009\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('renken-mumby-modellingthedynamicsofcoralreefmacroalgaeusingabayesianbeliefnetworkapproach-2009')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach},\n type = {article},\n year = {2009},\n keywords = {Bayesian belief network,Diadema antillarum,Dictyota spp.,Grazing pressure,Macroalgal dynamics,Nutrients,Scaridae},\n pages = {1305-1314},\n volume = {220},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380009001525},\n month = {5},\n id = {7499dc3b-15d2-3b2c-8cc1-7623649b11d9},\n created = {2015-04-12T20:17:32.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Macroalgae are a major benthic component of coral reefs and their dynamics influence the resilience of coral reefs to disturbance. However, the relative importance of physical and ecological processes in driving macroalgal dynamics is poorly understood. Here we develop a Bayesian belief network (BBN) model to integrate many of these processes and predict the growth of coral reef macroalgae. Bayesian belief networks use probabilistic relationships rather than deterministic rules to quantify the cause and effect assumptions. The model was developed using both new empirical data and quantified relationships elicited from previous studies. We demonstrate the efficacy of the BBN to predict the dynamics of a common Caribbean macroalgal genus Dictyota. Predictions of the model have an average accuracy of 55% (implying that 55% of the predicted categories of Dictyota cover were assigned to the correct class). Sensitivity analysis suggested that macroalgal dynamics were primarily driven by top–down processes of grazing rather than bottom–up nutrification. BBNs provide a useful framework for modelling complex systems, identifying gaps in our scientific understanding and communicating the complexities of the associated uncertainties in an explicit manner to stakeholders. We anticipate that accuracies will improve as new data are added to the model.},\n bibtype = {article},\n author = {Renken, Henk and Mumby, Peter J.},\n doi = {10.1016/j.ecolmodel.2009.02.022},\n journal = {Ecological Modelling},\n number = {9-10}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Macroalgae are a major benthic component of coral reefs and their dynamics influence the resilience of coral reefs to disturbance. However, the relative importance of physical and ecological processes in driving macroalgal dynamics is poorly understood. Here we develop a Bayesian belief network (BBN) model to integrate many of these processes and predict the growth of coral reef macroalgae. Bayesian belief networks use probabilistic relationships rather than deterministic rules to quantify the cause and effect assumptions. The model was developed using both new empirical data and quantified relationships elicited from previous studies. We demonstrate the efficacy of the BBN to predict the dynamics of a common Caribbean macroalgal genus Dictyota. Predictions of the model have an average accuracy of 55% (implying that 55% of the predicted categories of Dictyota cover were assigned to the correct class). Sensitivity analysis suggested that macroalgal dynamics were primarily driven by top–down processes of grazing rather than bottom–up nutrification. BBNs provide a useful framework for modelling complex systems, identifying gaps in our scientific understanding and communicating the complexities of the associated uncertainties in an explicit manner to stakeholders. We anticipate that accuracies will improve as new data are added to the model.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2008\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2008')\"\n      onkeypress=\"toggleGroup('group_2008')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2008</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"vangerven-taal-lucas-dynamicbayesiannetworksasprognosticmodelsforclinicalpatientmanagement-2008\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046408000154\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'vangerven-taal-lucas-dynamicbayesiannetworksasprognosticmodelsforclinicalpatientmanagement-2008', 'http://www.sciencedirect.com/science/article/pii/S1532046408000154', event);\">\n    Dynamic Bayesian networks as prognostic models for clinical patient management.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  van Gerven,  M., A., J.; Taal,  B., G.;  and Lucas,  P., J., F.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of biomedical informatics</i>, 41(4): 515-29. 8 2008.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046408000154\"\n     onclick=\"log_download('vangerven-taal-lucas-dynamicbayesiannetworksasprognosticmodelsforclinicalpatientmanagement-2008', 'http://www.sciencedirect.com/science/article/pii/S1532046408000154', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Dynamic Bayesian networks as prognostic models for clinical patient management. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jbi.2008.01.006\"\n     onclick=\"log_download('vangerven-taal-lucas-dynamicbayesiannetworksasprognosticmodelsforclinicalpatientmanagement-2008', 'http://doi.org/10.1016/j.jbi.2008.01.006', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vangerven-taal-lucas-dynamicbayesiannetworksasprognosticmodelsforclinicalpatientmanagement-2008\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('vangerven-taal-lucas-dynamicbayesiannetworksasprognosticmodelsforclinicalpatientmanagement-2008')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Dynamic Bayesian networks as prognostic models for clinical patient management.},\n type = {article},\n year = {2008},\n keywords = {Algorithms,Bayes Theorem,Carcinoid Tumor,Carcinoid Tumor: diagnosis,Carcinoid Tumor: mortality,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Neural Networks (Computer),Prognosis,Risk Assessment,Risk Assessment: methods,Risk Factors,Survival Analysis,Survival Rate},\n pages = {515-29},\n volume = {41},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046408000154},\n month = {8},\n id = {ce5b78cb-e69e-3915-8050-fc5b21a5b41c},\n created = {2015-04-12T20:17:33.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Prognostic models in medicine are usually been built using simple decision rules, proportional hazards models, or Markov models. Dynamic Bayesian networks (DBNs) offer an approach that allows for the incorporation of the causal and temporal nature of medical domain knowledge as elicited from domain experts, thereby allowing for detailed prognostic predictions. The aim of this paper is to describe the considerations that must be taken into account when constructing a DBN for complex medical domains and to demonstrate their usefulness in practice. To this end, we focus on the construction of a DBN for prognosis of carcinoid patients, compare performance with that of a proportional hazards model, and describe predictions for three individual patients. We show that the DBN can make detailed predictions, about not only patient survival, but also other variables of interest, such as disease progression, the effect of treatment, and the development of complications. Strengths and limitations of our approach are discussed and compared with those offered by traditional methods.},\n bibtype = {article},\n author = {van Gerven, Marcel A J and Taal, Babs G and Lucas, Peter J F},\n doi = {10.1016/j.jbi.2008.01.006},\n journal = {Journal of biomedical informatics},\n number = {4}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Prognostic models in medicine are usually been built using simple decision rules, proportional hazards models, or Markov models. Dynamic Bayesian networks (DBNs) offer an approach that allows for the incorporation of the causal and temporal nature of medical domain knowledge as elicited from domain experts, thereby allowing for detailed prognostic predictions. The aim of this paper is to describe the considerations that must be taken into account when constructing a DBN for complex medical domains and to demonstrate their usefulness in practice. To this end, we focus on the construction of a DBN for prognosis of carcinoid patients, compare performance with that of a proportional hazards model, and describe predictions for three individual patients. We show that the DBN can make detailed predictions, about not only patient survival, but also other variables of interest, such as disease progression, the effect of treatment, and the development of complications. Strengths and limitations of our approach are discussed and compared with those offered by traditional methods.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2007\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2007')\"\n      onkeypress=\"toggleGroup('group_2007')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2007</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"cruzramrez-acostamesa-carrillocalvet-navafernndez-barrientosmartnez-diagnosisofbreastcancerusingbayesiannetworksacasestudy-2007\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482507000340\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'cruzramrez-acostamesa-carrillocalvet-navafernndez-barrientosmartnez-diagnosisofbreastcancerusingbayesiannetworksacasestudy-2007', 'http://www.sciencedirect.com/science/article/pii/S0010482507000340', event);\">\n    Diagnosis of breast cancer using Bayesian networks: a case study.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Cruz-Ramírez,  N.; Acosta-Mesa,  H., G.; Carrillo-Calvet,  H.; Nava-Fernández,  L., A.;  and Barrientos-Martínez,  R., E.\n</span>\n\n  \n\n</span>\n\n  <i>Computers in biology and medicine</i>, 37(11): 1553-64. 11 2007.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482507000340\"\n     onclick=\"log_download('cruzramrez-acostamesa-carrillocalvet-navafernndez-barrientosmartnez-diagnosisofbreastcancerusingbayesiannetworksacasestudy-2007', 'http://www.sciencedirect.com/science/article/pii/S0010482507000340', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Diagnosis of breast cancer using Bayesian networks: a case study. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.compbiomed.2007.02.003\"\n     onclick=\"log_download('cruzramrez-acostamesa-carrillocalvet-navafernndez-barrientosmartnez-diagnosisofbreastcancerusingbayesiannetworksacasestudy-2007', 'http://doi.org/10.1016/j.compbiomed.2007.02.003', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/cruzramrez-acostamesa-carrillocalvet-navafernndez-barrientosmartnez-diagnosisofbreastcancerusingbayesiannetworksacasestudy-2007\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('cruzramrez-acostamesa-carrillocalvet-navafernndez-barrientosmartnez-diagnosisofbreastcancerusingbayesiannetworksacasestudy-2007')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Diagnosis of breast cancer using Bayesian networks: a case study.},\n type = {article},\n year = {2007},\n keywords = {Algorithms,Bayes Theorem,Biopsy, Fine-Needle,Breast Neoplasms,Breast Neoplasms: diagnosis,Cytodiagnosis,Cytodiagnosis: statistics &amp; numerical data,Databases, Factual,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: statistics &amp; numeric,Female,Humans,Observer Variation},\n pages = {1553-64},\n volume = {37},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482507000340},\n month = {11},\n id = {c166c75a-987f-386e-9dbf-98e6a0dc37eb},\n created = {2015-04-12T18:44:13.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We evaluate the effectiveness of seven Bayesian network classifiers as potential tools for the diagnosis of breast cancer using two real-world databases containing fine-needle aspiration of the breast lesion cases collected by a single observer and multiple observers, respectively. The results show a certain ingredient of subjectivity implicitly contained in these data: we get an average accuracy of 93.04% for the former and 83.31% for the latter. These findings suggest that observers see different things when looking at the samples in the microscope; a situation that significantly diminishes the performance of these classifiers in diagnosing such a disease.},\n bibtype = {article},\n author = {Cruz-Ramírez, Nicandro and Acosta-Mesa, Héctor Gabriel and Carrillo-Calvet, Humberto and Nava-Fernández, Luis Alonso and Barrientos-Martínez, Rocío Erandi},\n doi = {10.1016/j.compbiomed.2007.02.003},\n journal = {Computers in biology and medicine},\n number = {11}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We evaluate the effectiveness of seven Bayesian network classifiers as potential tools for the diagnosis of breast cancer using two real-world databases containing fine-needle aspiration of the breast lesion cases collected by a single observer and multiple observers, respectively. The results show a certain ingredient of subjectivity implicitly contained in these data: we get an average accuracy of 93.04% for the former and 83.31% for the latter. These findings suggest that observers see different things when looking at the samples in the microscope; a situation that significantly diminishes the performance of these classifiers in diagnosing such a disease.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"gilfeather-hamine-helman-hutt-loring-lyons-veroff-learningandmodelingbiosignaturesfromtissueimages-2007\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482507000339\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'gilfeather-hamine-helman-hutt-loring-lyons-veroff-learningandmodelingbiosignaturesfromtissueimages-2007', 'http://www.sciencedirect.com/science/article/pii/S0010482507000339', event);\">\n    Learning and modeling biosignatures from tissue images.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gilfeather,  F.; Hamine,  V.; Helman,  P.; Hutt,  J.; Loring,  T.; Lyons,  C., R.;  and Veroff,  R.\n</span>\n\n  \n\n</span>\n\n  <i>Computers in biology and medicine</i>, 37(11): 1539-52. 11 2007.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482507000339\"\n     onclick=\"log_download('gilfeather-hamine-helman-hutt-loring-lyons-veroff-learningandmodelingbiosignaturesfromtissueimages-2007', 'http://www.sciencedirect.com/science/article/pii/S0010482507000339', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Learning and modeling biosignatures from tissue images. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.compbiomed.2007.02.005\"\n     onclick=\"log_download('gilfeather-hamine-helman-hutt-loring-lyons-veroff-learningandmodelingbiosignaturesfromtissueimages-2007', 'http://doi.org/10.1016/j.compbiomed.2007.02.005', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gilfeather-hamine-helman-hutt-loring-lyons-veroff-learningandmodelingbiosignaturesfromtissueimages-2007\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gilfeather-hamine-helman-hutt-loring-lyons-veroff-learningandmodelingbiosignaturesfromtissueimages-2007')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Learning and modeling biosignatures from tissue images.},\n type = {article},\n year = {2007},\n keywords = {Algorithms,Animals,Artificial Intelligence,Bayes Theorem,Computer Simulation,Diagnosis, Computer-Assisted,Humans,Image Interpretation, Computer-Assisted,Infection,Infection: classification,Infection: diagnosis,Lung,Lung Diseases,Lung Diseases: diagnosis,Lung: anatomy &amp; histology,Mice},\n pages = {1539-52},\n volume = {37},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482507000339},\n month = {11},\n id = {57bfce04-027f-341a-a291-194cee674ec5},\n created = {2015-04-12T18:51:29.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Ideally biosignatures can be detected at the early infection phase and used both for developing diagnostic patterns and for prognostic triage. Such biosignatures are important for vaccine validation and to provide risk stratification to a population such as for the identification of individuals who are exposed to biological or chemical agents and who are at high risk for developing an infection. The research goal is to detect broad based biosignature models and is initially focused on developing effective computer-augmented pathology tied to animal models developed at the University of New Mexico (UNM). Using lung tissue from infected and nai ve mice, feature extraction from images of the tissue under a specialized microscope, and Bayesian networks to analyze the data sets of features, we were able to differentiate normal from diseased samples and viral from bacterial samples in mid to late stages of infection. This effort has shown the potential effectiveness of computer-augmented pathology in this application. The extended research intends to couple analysis of serum, microarray analysis of organs, proteomic data and the pathology. The rational for the current invasive procedure on animal models is to facilitate the development of data analysis and machine learning techniques that can eventually be generalized to the task of discovering non-invasive and early stage biosignatures for human models.},\n bibtype = {article},\n author = {Gilfeather, Frank and Hamine, Vikas and Helman, Paul and Hutt, Julie and Loring, Terry and Lyons, C Rick and Veroff, Robert},\n doi = {10.1016/j.compbiomed.2007.02.005},\n journal = {Computers in biology and medicine},\n number = {11}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Ideally biosignatures can be detected at the early infection phase and used both for developing diagnostic patterns and for prognostic triage. Such biosignatures are important for vaccine validation and to provide risk stratification to a population such as for the identification of individuals who are exposed to biological or chemical agents and who are at high risk for developing an infection. The research goal is to detect broad based biosignature models and is initially focused on developing effective computer-augmented pathology tied to animal models developed at the University of New Mexico (UNM). Using lung tissue from infected and nai ve mice, feature extraction from images of the tissue under a specialized microscope, and Bayesian networks to analyze the data sets of features, we were able to differentiate normal from diseased samples and viral from bacterial samples in mid to late stages of infection. This effort has shown the potential effectiveness of computer-augmented pathology in this application. The extended research intends to couple analysis of serum, microarray analysis of organs, proteomic data and the pathology. The rational for the current invasive procedure on animal models is to facilitate the development of data analysis and machine learning techniques that can eventually be generalized to the task of discovering non-invasive and early stage biosignatures for human models.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"verduijn-peek-rosseel-dejonge-demol-prognosticbayesiannetworksirationalelearningprocedureandclinicaluse-2007\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046407000615\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'verduijn-peek-rosseel-dejonge-demol-prognosticbayesiannetworksirationalelearningprocedureandclinicaluse-2007', 'http://www.sciencedirect.com/science/article/pii/S1532046407000615', event);\">\n    Prognostic Bayesian networks I: rationale, learning procedure, and clinical use.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Verduijn,  M.; Peek,  N.; Rosseel,  P., M., J.; de Jonge,  E.;  and de Mol,  B., A., J., M.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of biomedical informatics</i>, 40(6): 609-18. 12 2007.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046407000615\"\n     onclick=\"log_download('verduijn-peek-rosseel-dejonge-demol-prognosticbayesiannetworksirationalelearningprocedureandclinicaluse-2007', 'http://www.sciencedirect.com/science/article/pii/S1532046407000615', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Prognostic Bayesian networks I: rationale, learning procedure, and clinical use. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jbi.2007.07.003\"\n     onclick=\"log_download('verduijn-peek-rosseel-dejonge-demol-prognosticbayesiannetworksirationalelearningprocedureandclinicaluse-2007', 'http://doi.org/10.1016/j.jbi.2007.07.003', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/verduijn-peek-rosseel-dejonge-demol-prognosticbayesiannetworksirationalelearningprocedureandclinicaluse-2007\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('verduijn-peek-rosseel-dejonge-demol-prognosticbayesiannetworksirationalelearningprocedureandclinicaluse-2007')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Prognostic Bayesian networks I: rationale, learning procedure, and clinical use.},\n type = {article},\n year = {2007},\n keywords = {Algorithms,Artificial Intelligence,Bayes Theorem,Computer Simulation,Decision Support Systems, Clinical,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Humans,Models, Biological,Models, Statistical,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Prognosis},\n pages = {609-18},\n volume = {40},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046407000615},\n month = {12},\n id = {3a2e1100-6966-3420-a67d-82df563416d6},\n created = {2015-04-12T20:17:34.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Prognostic models are tools to predict the future outcome of disease and disease treatment, one of the fundamental tasks in clinical medicine. This article presents the prognostic Bayesian network (PBN) as a new type of prognostic model that builds on the Bayesian network methodology, and implements a dynamic, process-oriented view on prognosis. A PBN describes the mutual relationships between variables that come into play during subsequent stages of a care process and a clinical outcome. A dedicated procedure for inducing these networks from clinical data is presented. In this procedure, the network is composed of a collection of local supervised learning models that are recursively learned from the data. The procedure optimizes performance of the network&#x27;s primary task, outcome prediction, and handles the fact that patients may drop out of the process in earlier stages. Furthermore, the article describes how PBNs can be applied to solve a number of information problems that are related to medical prognosis.},\n bibtype = {article},\n author = {Verduijn, Marion and Peek, Niels and Rosseel, Peter M J and de Jonge, Evert and de Mol, Bas A J M},\n doi = {10.1016/j.jbi.2007.07.003},\n journal = {Journal of biomedical informatics},\n number = {6}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Prognostic models are tools to predict the future outcome of disease and disease treatment, one of the fundamental tasks in clinical medicine. This article presents the prognostic Bayesian network (PBN) as a new type of prognostic model that builds on the Bayesian network methodology, and implements a dynamic, process-oriented view on prognosis. A PBN describes the mutual relationships between variables that come into play during subsequent stages of a care process and a clinical outcome. A dedicated procedure for inducing these networks from clinical data is presented. In this procedure, the network is composed of a collection of local supervised learning models that are recursively learned from the data. The procedure optimizes performance of the network's primary task, outcome prediction, and handles the fact that patients may drop out of the process in earlier stages. Furthermore, the article describes how PBNs can be applied to solve a number of information problems that are related to medical prognosis.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2006\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2006')\"\n      onkeypress=\"toggleGroup('group_2006')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2006</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"aspinall-carniel-jaquet-woo-hincks-usinghiddenmultistatemarkovmodelswithmultiparametervolcanicdatatoprovideempiricalevidenceforalertleveldecisionsupport-2006\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0377027305003872\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'aspinall-carniel-jaquet-woo-hincks-usinghiddenmultistatemarkovmodelswithmultiparametervolcanicdatatoprovideempiricalevidenceforalertleveldecisionsupport-2006', 'http://www.sciencedirect.com/science/article/pii/S0377027305003872', event);\">\n    Using hidden multi-state Markov models with multi-parameter volcanic data to provide empirical evidence for alert level decision-support.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Aspinall,  W.; Carniel,  R.; Jaquet,  O.; Woo,  G.;  and Hincks,  T.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Volcanology and Geothermal Research</i>, 153(1-2): 112-124. 5 2006.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0377027305003872\"\n     onclick=\"log_download('aspinall-carniel-jaquet-woo-hincks-usinghiddenmultistatemarkovmodelswithmultiparametervolcanicdatatoprovideempiricalevidenceforalertleveldecisionsupport-2006', 'http://www.sciencedirect.com/science/article/pii/S0377027305003872', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Using hidden multi-state Markov models with multi-parameter volcanic data to provide empirical evidence for alert level decision-support [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jvolgeores.2005.08.010\"\n     onclick=\"log_download('aspinall-carniel-jaquet-woo-hincks-usinghiddenmultistatemarkovmodelswithmultiparametervolcanicdatatoprovideempiricalevidenceforalertleveldecisionsupport-2006', 'http://doi.org/10.1016/j.jvolgeores.2005.08.010', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/aspinall-carniel-jaquet-woo-hincks-usinghiddenmultistatemarkovmodelswithmultiparametervolcanicdatatoprovideempiricalevidenceforalertleveldecisionsupport-2006\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('aspinall-carniel-jaquet-woo-hincks-usinghiddenmultistatemarkovmodelswithmultiparametervolcanicdatatoprovideempiricalevidenceforalertleveldecisionsupport-2006')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Using hidden multi-state Markov models with multi-parameter volcanic data to provide empirical evidence for alert level decision-support},\n type = {article},\n year = {2006},\n keywords = {Bayesian Belief Network,eruption forecasting,evidence science,hidden Markov model,volcanology},\n pages = {112-124},\n volume = {153},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377027305003872},\n month = {5},\n id = {712032b5-7ec0-3ca8-93e3-be39f0fb306a},\n created = {2015-04-12T18:30:34.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {For the purposes of eruption forecasting and hazard mitigation, a volcanic crisis may be represented as a staged progression of states of unrest, each with its own timescale and likelihood of transition to other states (or to climactic eruption). If the state conditions can be interpreted physically, e.g., in terms of advancing materials failure, this knowledge could be used directly to inform decisions on alert level setting. A multi-state Markov process provides one simple model for defining states and for estimating rates of switching between states. However, for eruptive processes, such states are not directly observable and must be inferred from latent markers, such as seismic activity, gas output, deformation rates, etc., some of which may be contradictory. Interpretations of uncertain data will be liable to error, so a model is needed which can simultaneously estimate both elements: the transition likelihood of a hidden process and the probabilities of state misclassification. We describe the concept and underlying principles of continuous-time hidden Markov models and demonstrate them in a decision-support context with a preliminary working implementation using MULTIMO data. Where multi-parameter streams of raw, processed or conditioned data of different kinds are available, these can be input in near real-time to appropriate hidden multi-state Markov models, the outputs of each providing their own objective analyses of eruptive state in probabilistic terms. These separate, multiple indicators of state can then be input into a Bayesian Belief Network framework for weighing and combining them as different strands of evidence, together with other observations, data, interpretations and expert judgment.},\n bibtype = {article},\n author = {Aspinall, W.P. and Carniel, R. and Jaquet, O. and Woo, G. and Hincks, T.},\n doi = {10.1016/j.jvolgeores.2005.08.010},\n journal = {Journal of Volcanology and Geothermal Research},\n number = {1-2}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  For the purposes of eruption forecasting and hazard mitigation, a volcanic crisis may be represented as a staged progression of states of unrest, each with its own timescale and likelihood of transition to other states (or to climactic eruption). If the state conditions can be interpreted physically, e.g., in terms of advancing materials failure, this knowledge could be used directly to inform decisions on alert level setting. A multi-state Markov process provides one simple model for defining states and for estimating rates of switching between states. However, for eruptive processes, such states are not directly observable and must be inferred from latent markers, such as seismic activity, gas output, deformation rates, etc., some of which may be contradictory. Interpretations of uncertain data will be liable to error, so a model is needed which can simultaneously estimate both elements: the transition likelihood of a hidden process and the probabilities of state misclassification. We describe the concept and underlying principles of continuous-time hidden Markov models and demonstrate them in a decision-support context with a preliminary working implementation using MULTIMO data. Where multi-parameter streams of raw, processed or conditioned data of different kinds are available, these can be input in near real-time to appropriate hidden multi-state Markov models, the outputs of each providing their own objective analyses of eruptive state in probabilistic terms. These separate, multiple indicators of state can then be input into a Bayesian Belief Network framework for weighing and combining them as different strands of evidence, together with other observations, data, interpretations and expert judgment.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2004\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2004')\"\n      onkeypress=\"toggleGroup('group_2004')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2004</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"malka-lerner-classificationoffluorescenceinsituhybridizationimagesusingbeliefnetworks-2004\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0167865504001710\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'malka-lerner-classificationoffluorescenceinsituhybridizationimagesusingbeliefnetworks-2004', 'http://www.sciencedirect.com/science/article/pii/S0167865504001710', event);\">\n    Classification of fluorescence in situ hybridization images using belief networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Malka,  R.;  and Lerner,  B.\n</span>\n\n  \n\n</span>\n\n  <i>Pattern Recognition Letters</i>, 25(16): 1777-1785. 12 2004.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0167865504001710\"\n     onclick=\"log_download('malka-lerner-classificationoffluorescenceinsituhybridizationimagesusingbeliefnetworks-2004', 'http://www.sciencedirect.com/science/article/pii/S0167865504001710', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Classification of fluorescence in situ hybridization images using belief networks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.patrec.2004.07.010\"\n     onclick=\"log_download('malka-lerner-classificationoffluorescenceinsituhybridizationimagesusingbeliefnetworks-2004', 'http://doi.org/10.1016/j.patrec.2004.07.010', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/malka-lerner-classificationoffluorescenceinsituhybridizationimagesusingbeliefnetworks-2004\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('malka-lerner-classificationoffluorescenceinsituhybridizationimagesusingbeliefnetworks-2004')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Classification of fluorescence in situ hybridization images using belief networks},\n type = {article},\n year = {2004},\n keywords = {Belief networks,Fluorescence in situ hybridization (FISH),Image classification,K2 algorithm,Naive Bayesian classifier},\n pages = {1777-1785},\n volume = {25},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167865504001710},\n month = {12},\n id = {3f97b562-8d2e-32ad-8dbe-3bef87896c76},\n created = {2015-04-12T19:47:10.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning.},\n bibtype = {article},\n author = {Malka, Roy and Lerner, Boaz},\n doi = {10.1016/j.patrec.2004.07.010},\n journal = {Pattern Recognition Letters},\n number = {16}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2002\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2002')\"\n      onkeypress=\"toggleGroup('group_2002')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2002</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"hammond-ellis-ametaassessmentforelasmobranchsbasedondietarydataandbayesiannetworks-2002\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1470160X02000055\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'hammond-ellis-ametaassessmentforelasmobranchsbasedondietarydataandbayesiannetworks-2002', 'http://www.sciencedirect.com/science/article/pii/S1470160X02000055', event);\">\n    A meta-assessment for elasmobranchs based on dietary data and Bayesian networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Hammond,  T.;  and Ellis,  J.\n</span>\n\n  \n\n</span>\n\n  <i>Ecological Indicators</i>, 1(3): 197-211. 3 2002.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1470160X02000055\"\n     onclick=\"log_download('hammond-ellis-ametaassessmentforelasmobranchsbasedondietarydataandbayesiannetworks-2002', 'http://www.sciencedirect.com/science/article/pii/S1470160X02000055', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A meta-assessment for elasmobranchs based on dietary data and Bayesian networks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/S1470-160X(02)00005-5\"\n     onclick=\"log_download('hammond-ellis-ametaassessmentforelasmobranchsbasedondietarydataandbayesiannetworks-2002', 'http://doi.org/10.1016/S1470-160X(02)00005-5', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/hammond-ellis-ametaassessmentforelasmobranchsbasedondietarydataandbayesiannetworks-2002\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('hammond-ellis-ametaassessmentforelasmobranchsbasedondietarydataandbayesiannetworks-2002')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A meta-assessment for elasmobranchs based on dietary data and Bayesian networks},\n type = {article},\n year = {2002},\n keywords = {Bayesian networks,Elasmobranchs,Fisheries management,Food webs,Threatened species},\n pages = {197-211},\n volume = {1},\n websites = {http://www.sciencedirect.com/science/article/pii/S1470160X02000055},\n month = {3},\n id = {043feed1-8366-3a45-a06f-512aa682a2de},\n created = {2015-04-12T18:51:29.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We developed a new approach, meta-assessment, as a tool for identifying declining (and potentially threatened) fish stocks in situations where a lack of data precludes traditional stock assessments. Meta-assessments are models enhanced by the incorporation of other stock assessment results. We used this idea to estimate historic biomass trends for demersal elasmobranchs of the Irish Sea. Bayesian networks, constructed from published dietary data and resembling food webs, allowed us to incorporate into our estimates the results from virtual population analysis (VPA) for Irish Sea cod, sole, plaice and whiting. To assess accuracy, we used cross-validation, estimating historic biomass trends in each individual VPA species from trends in the other three plus trends in fishing effort. We compared predicted annual trends to those derived from VPA and found 66% accuracy. We also compared biomass trends estimated from annual trawl surveys to corresponding network predictions, recovering survey trends correctly 61% of the time for elasmobranchs, 78% of the time for gurnards (Triglidae) and 89% for bib and pout (Trisopterus spp.). Results suggest that of the 11 elasmobranchs examined, the angel shark (Squatina squatina) increased in biomass least frequently from 1987 to 1997, a view consistent with survey results. Our approach also suggested a marked decline in common skate (Dipturus batis) over the period 1965–1978, during which time the skate was extirpated from the Irish Sea. We conclude that meta-assessment can serve as an exploratory method for identifying potentially threatened stocks, where even landings data are unavailable.},\n bibtype = {article},\n author = {Hammond, T.R and Ellis, J.R},\n doi = {10.1016/S1470-160X(02)00005-5},\n journal = {Ecological Indicators},\n number = {3}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We developed a new approach, meta-assessment, as a tool for identifying declining (and potentially threatened) fish stocks in situations where a lack of data precludes traditional stock assessments. Meta-assessments are models enhanced by the incorporation of other stock assessment results. We used this idea to estimate historic biomass trends for demersal elasmobranchs of the Irish Sea. Bayesian networks, constructed from published dietary data and resembling food webs, allowed us to incorporate into our estimates the results from virtual population analysis (VPA) for Irish Sea cod, sole, plaice and whiting. To assess accuracy, we used cross-validation, estimating historic biomass trends in each individual VPA species from trends in the other three plus trends in fishing effort. We compared predicted annual trends to those derived from VPA and found 66% accuracy. We also compared biomass trends estimated from annual trawl surveys to corresponding network predictions, recovering survey trends correctly 61% of the time for elasmobranchs, 78% of the time for gurnards (Triglidae) and 89% for bib and pout (Trisopterus spp.). Results suggest that of the 11 elasmobranchs examined, the angel shark (Squatina squatina) increased in biomass least frequently from 1987 to 1997, a view consistent with survey results. Our approach also suggested a marked decline in common skate (Dipturus batis) over the period 1965–1978, during which time the skate was extirpated from the Irish Sea. We conclude that meta-assessment can serve as an exploratory method for identifying potentially threatened stocks, where even landings data are unavailable.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2000\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2000')\"\n      onkeypress=\"toggleGroup('group_2000')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2000</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"mckendrick-gettinby-gu-reid-revie-usingabayesianbeliefnetworktoaiddifferentialdiagnosisoftropicalbovinediseases-2000\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0167587700001720\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mckendrick-gettinby-gu-reid-revie-usingabayesianbeliefnetworktoaiddifferentialdiagnosisoftropicalbovinediseases-2000', 'http://www.sciencedirect.com/science/article/pii/S0167587700001720', event);\">\n    Using a Bayesian belief network to aid differential diagnosis of tropical bovine diseases.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  McKendrick,  I.; Gettinby,  G.; Gu,  Y.; Reid,  S.;  and Revie,  C.\n</span>\n\n  \n\n</span>\n\n  <i>Preventive Veterinary Medicine</i>, 47(3): 141-156. 11 2000.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0167587700001720\"\n     onclick=\"log_download('mckendrick-gettinby-gu-reid-revie-usingabayesianbeliefnetworktoaiddifferentialdiagnosisoftropicalbovinediseases-2000', 'http://www.sciencedirect.com/science/article/pii/S0167587700001720', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Using a Bayesian belief network to aid differential diagnosis of tropical bovine diseases [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/S0167-5877(00)00172-0\"\n     onclick=\"log_download('mckendrick-gettinby-gu-reid-revie-usingabayesianbeliefnetworktoaiddifferentialdiagnosisoftropicalbovinediseases-2000', 'http://doi.org/10.1016/S0167-5877(00)00172-0', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mckendrick-gettinby-gu-reid-revie-usingabayesianbeliefnetworktoaiddifferentialdiagnosisoftropicalbovinediseases-2000\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mckendrick-gettinby-gu-reid-revie-usingabayesianbeliefnetworktoaiddifferentialdiagnosisoftropicalbovinediseases-2000')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Using a Bayesian belief network to aid differential diagnosis of tropical bovine diseases},\n type = {article},\n year = {2000},\n keywords = {Africa,Bayesian belief network,Cattle disease,Differential diagnosis,Expert system},\n pages = {141-156},\n volume = {47},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587700001720},\n month = {11},\n id = {c6fb9f5d-9401-3d6e-a6ec-1fc41a7a7912},\n created = {2015-04-12T20:17:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2017-03-14T14:38:49.606Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The examination of presenting signs has always played an important role in the diagnosis of diseases in animal populations. In the case of diseases of tropical cattle, such expertise is often scarce and confined to those experts with many years of experience. To capture, conserve and disseminate such valuable expert knowledge remains a key challenge to the application of knowledge-based systems in veterinary medicine. In this communication, we explore the use of a Bayesian belief network to quantify expert opinion with a view to estimating the likelihood of various diseases in the presence and absence of certain signs. Information was elicited from a panel of 44 experienced veterinarians to provide the response matrix of 27 signs associated with 20 commonly occurring diseases in sub-Saharan cattle. Using this prior information, estimates of the probability of certain signs occurring with each disease were calculated from which the Bayesian belief network was able to propagate the posterior probability of each of the diseases based on the observed signs. The method as an aid in making diagnosis is discussed. It is recognised that such an approach is but one strand in the process of arriving at a diagnosis. For ease of use and accessibility, the approach has been converted into the software program CaDDiS (Cattle Disease Diagnosis System) which is available for consultation on the World Wide Web.},\n bibtype = {article},\n author = {McKendrick, I.J and Gettinby, G and Gu, Y and Reid, S.W.J and Revie, C.W},\n doi = {10.1016/S0167-5877(00)00172-0},\n journal = {Preventive Veterinary Medicine},\n number = {3}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The examination of presenting signs has always played an important role in the diagnosis of diseases in animal populations. In the case of diseases of tropical cattle, such expertise is often scarce and confined to those experts with many years of experience. To capture, conserve and disseminate such valuable expert knowledge remains a key challenge to the application of knowledge-based systems in veterinary medicine. In this communication, we explore the use of a Bayesian belief network to quantify expert opinion with a view to estimating the likelihood of various diseases in the presence and absence of certain signs. Information was elicited from a panel of 44 experienced veterinarians to provide the response matrix of 27 signs associated with 20 commonly occurring diseases in sub-Saharan cattle. Using this prior information, estimates of the probability of certain signs occurring with each disease were calculated from which the Bayesian belief network was able to propagate the posterior probability of each of the diseases based on the observed signs. The method as an aid in making diagnosis is discussed. It is recognised that such an approach is but one strand in the process of arriving at a diagnosis. For ease of use and accessibility, the approach has been converted into the software program CaDDiS (Cattle Disease Diagnosis System) which is available for consultation on the World Wide Web.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_1997\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_1997')\"\n      onkeypress=\"toggleGroup('group_1997')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>1997</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"friedman-geiger-goldszmidt-bayesiannetworkclassifiers-1997\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1023/A:1007465528199%5Cnpapers2://publication/doi/10.1023/A:1007465528199\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'friedman-geiger-goldszmidt-bayesiannetworkclassifiers-1997', 'http://dx.doi.org/10.1023/A:1007465528199%5Cnpapers2://publication/doi/10.1023/A:1007465528199', event);\">\n    Bayesian Network Classifiers.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Friedman,  N.; Geiger,  D.;  and Goldszmidt,  M.\n</span>\n\n  \n\n</span>\n\n  <i>Machine learning</i>, 29: 131-163.  1997.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1023/A:1007465528199%5Cnpapers2://publication/doi/10.1023/A:1007465528199\"\n     onclick=\"log_download('friedman-geiger-goldszmidt-bayesiannetworkclassifiers-1997', 'http://dx.doi.org/10.1023/A:1007465528199%5Cnpapers2://publication/doi/10.1023/A:1007465528199', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Bayesian Network Classifiers [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1023/A:1007465528199\"\n     onclick=\"log_download('friedman-geiger-goldszmidt-bayesiannetworkclassifiers-1997', 'http://doi.org/10.1023/A:1007465528199', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/friedman-geiger-goldszmidt-bayesiannetworkclassifiers-1997\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('friedman-geiger-goldszmidt-bayesiannetworkclassifiers-1997')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Bayesian Network Classifiers},\n type = {article},\n year = {1997},\n pages = {131-163},\n volume = {29},\n websites = {http://dx.doi.org/10.1023/A:1007465528199%5Cnpapers2://publication/doi/10.1023/A:1007465528199},\n id = {46fa1577-f67d-3e80-bbdd-4bccc555eee0},\n created = {2019-12-27T22:41:19.683Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2019-12-27T22:41:19.683Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4. 5. This fact raises the question of ...},\n bibtype = {article},\n author = {Friedman, Nir and Geiger, Dan and Goldszmidt, Moises},\n doi = {10.1023/A:1007465528199},\n journal = {Machine learning}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4. 5. This fact raises the question of ...\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_undefined\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_undefined')\"\n      onkeypress=\"toggleGroup('group_undefined')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>undefined</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"xie-estimationandapplicationofabayesiannetworkmodelfordiscretetravelchoiceanalysis\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Estimation and Application of a Bayesian Network Model for Discrete Travel Choice Analysis.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Xie,  C.\n</span>\n\n  \n\n</span>\n\n  <i>Transportation Letters</i>.  .\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/xie-estimationandapplicationofabayesiannetworkmodelfordiscretetravelchoiceanalysis\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('xie-estimationandapplicationofabayesiannetworkmodelfordiscretetravelchoiceanalysis')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Estimation and Application of a Bayesian Network Model for Discrete Travel Choice Analysis},\n type = {article},\n id = {56632ecf-ec53-36d3-9c3d-19ed3edf4ce4},\n created = {2020-01-06T20:18:13.290Z},\n accessed = {2020-01-06},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {379f32c7-d27b-3627-898a-9a4acebc265b},\n last_modified = {2020-01-06T20:18:13.378Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Xie, Chi},\n journal = {Transportation Letters}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);