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\n \n 2020\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n A probabilistic approach for economic evaluation of occupational health and safety interventions: a case study of silica exposure reduction interventions in the construction sector.\n \n \n \n \n\n\n \n Mofidi, A.; Tompa, E.; Mortazavi, S., B.; Esfahanipour, A.; and Demers, P., A.\n\n\n \n\n\n\n BMC Public Health, 20(1): 210. 12 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A probabilistic approach for economic evaluation of occupational health and safety interventions: a case study of silica exposure reduction interventions in the construction sector},\n type = {article},\n year = {2020},\n keywords = {Biostatistics,Environmental Health,Epidemiology,Medicine/Public Health,Public Health,Vaccine,general},\n pages = {210},\n volume = {20},\n websites = {https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-020-8307-7},\n month = {12},\n publisher = {BioMed Central},\n day = {11},\n id = {c753b410-a512-31ee-bd9a-91d974855c3e},\n created = {2020-02-17T21:50:36.514Z},\n accessed = {2020-02-17},\n file_attached = {true},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2020-02-17T21:50:36.637Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Construction workers are at a high risk of exposure to various types of hazardous substances such as crystalline silica. Though multiple studies indicate the evidence regarding the effectiveness of different silica exposure reduction interventions in the construction sector, the decisions for selecting a specific silica exposure reduction intervention are best informed by an economic evaluation. Economic evaluation of interventions is subjected to uncertainties in practice, mostly due to the lack of precise data on important variables. In this study, we aim to identify the most cost-beneficial silica exposure reduction intervention for the construction sector under uncertain situations. We apply a probabilistic modeling approach that covers a large number of variables relevant to the cost of lung cancer, as well as the costs of silica exposure reduction interventions. To estimate the societal lifetime cost of lung cancer, we use an incidence cost approach. To estimate the net benefit of each intervention, we compare the expected cost of lung cancer cases averted, with expected cost of implementation of the intervention in one calendar year. Sensitivity analysis is used to quantify how different variables affect interventions net benefit. A positive net benefit is expected for all considered interventions. The highest number of lung cancer cases are averted by combined use of wet method, local exhaust ventilation and personal protective equipment, about 107 cases, with expected net benefit of $45.9 million. Results also suggest that the level of exposure is an important determinant for the selection of the most cost-beneficial intervention. This study provides important insights for decision makers about silica exposure reduction interventions in the construction sector. It also provides an overview of the potential advantages of using probabilistic modeling approach to undertake economic evaluations, particularly when researchers are confronted with a large number of uncertain variables.},\n bibtype = {article},\n author = {Mofidi, Amirabbas and Tompa, Emile and Mortazavi, Seyed Bagher and Esfahanipour, Akbar and Demers, Paul A.},\n doi = {10.1186/s12889-020-8307-7},\n journal = {BMC Public Health},\n number = {1}\n}

\n

\n\n\n

\n Construction workers are at a high risk of exposure to various types of hazardous substances such as crystalline silica. Though multiple studies indicate the evidence regarding the effectiveness of different silica exposure reduction interventions in the construction sector, the decisions for selecting a specific silica exposure reduction intervention are best informed by an economic evaluation. Economic evaluation of interventions is subjected to uncertainties in practice, mostly due to the lack of precise data on important variables. In this study, we aim to identify the most cost-beneficial silica exposure reduction intervention for the construction sector under uncertain situations. We apply a probabilistic modeling approach that covers a large number of variables relevant to the cost of lung cancer, as well as the costs of silica exposure reduction interventions. To estimate the societal lifetime cost of lung cancer, we use an incidence cost approach. To estimate the net benefit of each intervention, we compare the expected cost of lung cancer cases averted, with expected cost of implementation of the intervention in one calendar year. Sensitivity analysis is used to quantify how different variables affect interventions net benefit. A positive net benefit is expected for all considered interventions. The highest number of lung cancer cases are averted by combined use of wet method, local exhaust ventilation and personal protective equipment, about 107 cases, with expected net benefit of $45.9 million. Results also suggest that the level of exposure is an important determinant for the selection of the most cost-beneficial intervention. This study provides important insights for decision makers about silica exposure reduction interventions in the construction sector. It also provides an overview of the potential advantages of using probabilistic modeling approach to undertake economic evaluations, particularly when researchers are confronted with a large number of uncertain variables.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management.\n \n \n \n \n\n\n \n Hosseini, M., M.; Zargoush, M.; Alemi, F.; and Kheirbek, R., E.\n\n\n \n\n\n\n Journal of Big Data, 7(1): 26. 12 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Leveraging$ Paper\n \n \n \n $\"Leveraging$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management},\n type = {article},\n year = {2020},\n keywords = {Communications Engineering,Computational Science and Engineering,Data Mining and Knowledge Discovery,Database Management,Information Storage and Retrieval,Mathematical Applications in Computer Science,Networks},\n pages = {26},\n volume = {7},\n websites = {https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00302-z},\n month = {12},\n publisher = {SpringerOpen},\n day = {10},\n id = {a3e055b3-488e-33c1-b447-e4311d1058df},\n created = {2020-04-15T13:04:39.268Z},\n accessed = {2020-04-15},\n file_attached = {true},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2020-04-15T13:04:39.391Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper proposes a novel algorithm for optimizing decision variables with respect to an outcome variable of interest in complex problems, such as those arising from big data. The proposed algorithm builds on the notion of Markov blankets in Bayesian networks to alleviate the computational challenges associated with optimization tasks in complex datasets. Through a case study, we apply the algorithm to optimize medication prescriptions for diabetic patients, who have different characteristics, suffer from multiple comorbidities, and take multiple medications concurrently. In particular, we demonstrate how the optimal combination of diabetic medications can be found by examining the comparative effectiveness of the medications among similar patients. The case study is based on 5 years of data for 19,223 diabetic patients. Our results indicate that certain patient characteristics (e.g., clinical and demographic features) influence optimal treatment decisions. Among patients examined, monotherapy with metformin was the most common optimal medication decision. The results are consistent with the relevant clinical guidelines and reports in the medical literature. The proposed algorithm obviates the need for knowledge of the whole Bayesian network model, which can be very complex in big data problems. The procedure can be applied to any complex Bayesian network with numerous features, multiple decision variables, and a target variable.},\n bibtype = {article},\n author = {Hosseini, Mahsa Madani and Zargoush, Manaf and Alemi, Farrokh and Kheirbek, Raya Elfadel},\n doi = {10.1186/s40537-020-00302-z},\n journal = {Journal of Big Data},\n number = {1}\n}

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\n \n 2019\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n Clinical and Statistical Validation of a Probabilistic Prediction Tool of Total Knee Arthroplasty Outcome.\n \n \n \n \n\n\n \n Twiggs, J., G.; Wakelin, E., A.; Fritsch, B., A.; Liu, D., W.; Solomon, M.; Parker, D.; Klasan, A.; and Miles, B.\n\n\n \n\n\n\n The Journal of Arthroplasty. 6 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Clinical$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{\n title = {Clinical and Statistical Validation of a Probabilistic Prediction Tool of Total Knee Arthroplasty Outcome},\n type = {article},\n year = {2019},\n websites = {https://www.sciencedirect.com/science/article/pii/S0883540319305583},\n month = {6},\n publisher = {Churchill Livingstone},\n day = {13},\n id = {87284e1b-bbd5-3b06-9cfe-2a65601a22c6},\n created = {2019-06-19T18:13:57.023Z},\n accessed = {2019-06-18},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2019-06-19T18:13:57.023Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {BACKGROUND\nA contingent of patients are dissatisfied with the results of Total Knee Arthroplasty (TKA). Predicting which patients are at elevated risk of a poor outcome would be useful in patient selection for TKA, and could reduce dissatisfaction in patients who do go on to TKA. Existing models to predict outcome have seen limited implementation as functional tools. This study aims to validate a model and shared decision-making tool for clinical utility and predictive accuracy. \n\nMETHODS\nA Bayesian Belief Network (BBN) statistical model was developed using data from the Osteoarthritis Initiative, an NIH funded observational study. Following internal validation, the model was implemented into a clinical tool used during patient consultations. A consecutive series of consultations for osteoarthritis before and after introduction of the tool was used to evaluate the clinical impact of the tool. A data audit of pre- and post-operative outcomes of TKA patients exposed to the tool was used to evaluate the accuracy of model predictions. \n\nRESULTS\nThe tool both impacted the decision for surgery and identified patients at risk of limited improvement. After introduction of the tool, patients booked for surgery reported statistically significantly worse KOOS pain scores (difference: 15.2, p<0.001) than those not booked, while there was no significant difference prior. When evaluating accuracy of the prediction, there was a 27% chance of not improving if predicted at risk, and a 1.4% chance if predicted to improve. This gives a risk ratio of 19x (p < 0.001) for patients not improving if predicted at risk by the tool. \n\nCONCLUSIONS\nFor a prediction tool to be clinically useful it needs to provide a better understanding of the likely clinical outcome of an intervention than existed without its use at the point that clinical decisions are to be made. The model presented here showed validation comparable to its contemporaries and meaningfully changed surgical practice when implemented into a clinical tool. With routine use, this tool has the potential to direct patients to surgical or non-surgical pathways on a patient specific bases, ensuring only patients who will benefit from TKA surgery are selected.},\n bibtype = {article},\n author = {Twiggs, Joshua G. and Wakelin, Edgar A. and Fritsch, Brett A. and Liu, David W. and Solomon, Michael and Parker, David and Klasan, Antonio and Miles, Brad},\n doi = {10.1016/J.ARTH.2019.06.007},\n journal = {The Journal of Arthroplasty}\n}

\n

\n\n\n

\n BACKGROUND\nA contingent of patients are dissatisfied with the results of Total Knee Arthroplasty (TKA). Predicting which patients are at elevated risk of a poor outcome would be useful in patient selection for TKA, and could reduce dissatisfaction in patients who do go on to TKA. Existing models to predict outcome have seen limited implementation as functional tools. This study aims to validate a model and shared decision-making tool for clinical utility and predictive accuracy. \n\nMETHODS\nA Bayesian Belief Network (BBN) statistical model was developed using data from the Osteoarthritis Initiative, an NIH funded observational study. Following internal validation, the model was implemented into a clinical tool used during patient consultations. A consecutive series of consultations for osteoarthritis before and after introduction of the tool was used to evaluate the clinical impact of the tool. A data audit of pre- and post-operative outcomes of TKA patients exposed to the tool was used to evaluate the accuracy of model predictions. \n\nRESULTS\nThe tool both impacted the decision for surgery and identified patients at risk of limited improvement. After introduction of the tool, patients booked for surgery reported statistically significantly worse KOOS pain scores (difference: 15.2, p<0.001) than those not booked, while there was no significant difference prior. When evaluating accuracy of the prediction, there was a 27% chance of not improving if predicted at risk, and a 1.4% chance if predicted to improve. This gives a risk ratio of 19x (p < 0.001) for patients not improving if predicted at risk by the tool. \n\nCONCLUSIONS\nFor a prediction tool to be clinically useful it needs to provide a better understanding of the likely clinical outcome of an intervention than existed without its use at the point that clinical decisions are to be made. The model presented here showed validation comparable to its contemporaries and meaningfully changed surgical practice when implemented into a clinical tool. With routine use, this tool has the potential to direct patients to surgical or non-surgical pathways on a patient specific bases, ensuring only patients who will benefit from TKA surgery are selected.\n

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\n \n\n \n \n \n \n \n \n Decision Support System for Mitigating Athletic Injuries.\n \n \n \n \n\n\n \n Peterson, K.; and Evans, L.\n\n\n \n\n\n\n International Journal of Computer Science in Sport, 18(1): 45-63. 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Decision$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Decision Support System for Mitigating Athletic Injuries},\n type = {article},\n year = {2019},\n pages = {45-63},\n volume = {18},\n websites = {http://content.sciendo.com/view/journals/ijcss/18/1/article-p45.xml},\n id = {771b6a17-5aed-397a-8249-afce7dbdabbe},\n created = {2019-08-25T13:16:07.899Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2019-08-25T13:16:07.899Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The purpose of the present study was to demonstrate an inductive approach for dynamically modelling sport-related injuries with a probabilistic graphical model. Dynamic Bayesian Network (DBN), a well-known machine learning method, was employed to illustrate how sport practitioners could utilize a simulatory environment to augment the training management process. 23 University of Iowa female student-athletes (from 3 undisclosed teams) were regularly monitored with common athlete monitoring technologies, throughout the 2016 competitive season, as a part of their routine health and well-being surveillance. The presented work investigated the ability of these technologies to model injury occurrences in a dynamic, temporal dimension. To verify validity, DBN model accuracy was compared with the performance of its static counterpart. After 3 rounds of 5-fold cross-validation, resultant DBN mean accuracy surpassed naïve baseline threshold whereas static Bayesian network did not achieve baseline accuracy. Conclusive DBN suggested subjectively-reported stress two days prior, subjective internal perceived exertions one day prior, direct current potential and sympathetic tone the day of, as the most impactful towards injury manifestation.},\n bibtype = {article},\n author = {Peterson, K.D. and Evans, L.C.},\n doi = {10.2478/ijcss-2019-0003},\n journal = {International Journal of Computer Science in Sport},\n number = {1}\n}

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\n \n 2018\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n Knowledge-Driven Interpretation of Multi-View Data in Medicine.\n \n \n \n \n\n\n \n Sudhir Pillai, P.; Feng, L.; and Leong, T.\n\n\n \n\n\n\n Studies in health technology and informatics, 247: 745-749. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Knowledge-Driven$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Knowledge-Driven Interpretation of Multi-View Data in Medicine.},\n type = {article},\n year = {2018},\n keywords = {Bayesian Networks,Clinical,Data Integration,Heterogeneous,Multi-view},\n pages = {745-749},\n volume = {247},\n websites = {http://www.ncbi.nlm.nih.gov/pubmed/29678060},\n id = {943e3d28-72b1-3397-b76e-e7a5ecb2bf23},\n created = {2018-05-29T00:02:43.481Z},\n accessed = {2018-04-28},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2018-05-29T00:02:43.481Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {We propose a new approach to clinical decision support with interpretable recommendations from multi-view data. We introduce a Bayesian network structure learning method to help identify the relevant factors and their relationships. Guided by minimal domain knowledge, this method highlights the significant associations among the demography, medical and family history, lifestyle, and biomarker data to facilitate informed clinical decisions. We demonstrate the effectiveness of the method for detecting mild neurocognitive disorder in the elderly from a real-life dataset in Singapore. The empirical results show that our method achieves better interpretability in addition to comparable accuracy with respect to the benchmark studies.},\n bibtype = {article},\n author = {Sudhir Pillai, Parvathy and Feng, Lei and Leong, Tze-Yun},\n journal = {Studies in health technology and informatics}\n}

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\n \n\n \n \n \n \n \n \n An image-guided radiotherapy decision-support framework incorporating a Bayesian network and visualization tool.\n \n \n \n \n\n\n \n Hargrave, C.; Deegan, T.; Bednarz, T.; Poulsen, M.; Harden, F.; and Mengersen, K.\n\n\n \n\n\n\n Medical Physics. 5 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {An image-guided radiotherapy decision-support framework incorporating a Bayesian network and visualization tool},\n type = {article},\n year = {2018},\n keywords = {Bayesian network,Cone‐beam computed tomography,Decision support,Image‐guided radiotherapy,Visualization tool},\n websites = {http://doi.wiley.com/10.1002/mp.12979},\n month = {5},\n publisher = {Wiley-Blackwell},\n day = {17},\n id = {f74fadc9-9dfa-362b-8599-4e54f56737b0},\n created = {2018-05-29T00:50:10.573Z},\n accessed = {2018-05-28},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2018-05-29T00:50:10.573Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Hargrave, Catriona and Deegan, Timothy and Bednarz, Tomasz and Poulsen, Michael and Harden, Fiona and Mengersen, Kerrie},\n doi = {10.1002/mp.12979},\n journal = {Medical Physics}\n}

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\n \n\n \n \n \n \n \n A Bayesian network based adaptability design of product structures for function evolution.\n \n \n \n\n\n \n Li, S.; Wu, Y.; Xu, Y.; Hu, J.; and Hu, J.\n\n\n \n\n\n\n Applied Sciences (Switzerland), 8(4). 3 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian network based adaptability design of product structures for function evolution},\n type = {article},\n year = {2018},\n keywords = {Adaptability design,Bayesian network,Data analysis,Product function evolution},\n volume = {8},\n month = {3},\n publisher = {MDPI AG},\n day = {26},\n id = {a9dbd24c-0ec8-3bc6-ae9b-b94550223617},\n created = {2019-08-26T13:16:23.861Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2019-08-26T13:16:23.989Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2018 by the authors. Structure adaptability design is critical for function evolution in product families, in which many structural and functional design factors are intertwined together with manufacturing cost, customer satisfaction, and final market sales. How to achieve a delicate balance among all of these factors to maximize the market performance of the product is too complicated to address based on traditional domain experts' knowledge or some ad hoc heuristics. Here, we propose a quantitative product evolution design model that is based on Bayesian networks to model the dynamic relationship between customer needs and product structure design. In our model, all of the structural or functional features along with customer satisfaction, manufacturing cost, sale price, market sales, and indirect factors are modeled as random variables denoted as nodes in the Bayesian networks. The structure of the Bayesian model is then determined based on the historical data, which captures the dynamic sophisticated relationship of customer demands of a product, structural design, and market performance. Application of our approach to an electric toothbrush product family evolution design problem shows that our model allows for designers to interrogate with the model and obtain theoretical and decision support for dynamic product feature design process.},\n bibtype = {article},\n author = {Li, Shaobo and Wu, Yongming and Xu, Yanxia and Hu, Jie and Hu, Jianjun},\n doi = {10.3390/app8040493},\n journal = {Applied Sciences (Switzerland)},\n number = {4}\n}

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\n \n\n \n \n \n \n \n \n A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution.\n \n \n \n \n\n\n \n Li, S.; Wu, Y.; Xu, Y.; Hu, J.; and Hu, J.\n\n\n \n\n\n\n Applied Sciences, 8(4): 493. 3 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution},\n type = {article},\n year = {2018},\n keywords = {Bayesian network,adaptability design,data analysis,product function evolution},\n pages = {493},\n volume = {8},\n websites = {http://www.mdpi.com/2076-3417/8/4/493},\n month = {3},\n publisher = {Multidisciplinary Digital Publishing Institute},\n day = {26},\n id = {37efe257-36cc-30ab-950e-900deb86e69c},\n created = {2019-10-01T00:56:10.727Z},\n accessed = {2019-09-30},\n file_attached = {true},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2019-10-01T00:56:10.842Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Structure adaptability design is critical for function evolution in product families, in which many structural and functional design factors are intertwined together with manufacturing cost, customer satisfaction, and final market sales. How to achieve a delicate balance among all of these factors to maximize the market performance of the product is too complicated to address based on traditional domain experts’ knowledge or some ad hoc heuristics. Here, we propose a quantitative product evolution design model that is based on Bayesian networks to model the dynamic relationship between customer needs and product structure design. In our model, all of the structural or functional features along with customer satisfaction, manufacturing cost, sale price, market sales, and indirect factors are modeled as random variables denoted as nodes in the Bayesian networks. The structure of the Bayesian model is then determined based on the historical data, which captures the dynamic sophisticated relationship of customer demands of a product, structural design, and market performance. Application of our approach to an electric toothbrush product family evolution design problem shows that our model allows for designers to interrogate with the model and obtain theoretical and decision support for dynamic product feature design process.},\n bibtype = {article},\n author = {Li, Shaobo and Wu, Yongming and Xu, Yanxia and Hu, Jie and Hu, Jianjun},\n doi = {10.3390/app8040493},\n journal = {Applied Sciences},\n number = {4}\n}

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\n \n 2016\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support.\n \n \n \n\n\n \n Constantinou, A., C.; Fenton, N.; Marsh, W.; and Radlinski, L.\n\n\n \n\n\n\n Artificial Intelligence in Medicine, 67. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support},\n type = {article},\n year = {2016},\n volume = {67},\n id = {32a05b62-5bfb-32aa-b0f4-db0b01a2c0eb},\n created = {2017-08-23T21:41:49.543Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-08-23T21:41:49.543Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Objectives: (1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. Method: The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. Results: When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. Conclusions: This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.},\n bibtype = {article},\n author = {Constantinou, Anthony Costa and Fenton, Norman and Marsh, William and Radlinski, Lukasz},\n doi = {10.1016/j.artmed.2016.01.002},\n journal = {Artificial Intelligence in Medicine}\n}

\n

\n\n\n

\n Objectives: (1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. Method: The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. Results: When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. Conclusions: This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.\n

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\n \n 2015\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n Operationalizing an ecosystem services-based approach using Bayesian Belief Networks: An application to riparian buffer strips.\n \n \n \n \n\n\n \n McVittie, A.; Norton, L.; Martin-Ortega, J.; Siameti, I.; Glenk, K.; and Aalders, I.\n\n\n \n\n\n\n Ecological Economics, 110: 15-27. 2 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Operationalizing$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Operationalizing an ecosystem services-based approach using Bayesian Belief Networks: An application to riparian buffer strips},\n type = {article},\n year = {2015},\n keywords = {Bayesian networks,Ecosystem services,Interdisciplinary research,Valuation},\n pages = {15-27},\n volume = {110},\n websites = {http://www.sciencedirect.com/science/article/pii/S0921800914003711},\n month = {2},\n id = {7ff15823-b682-3b53-94d9-369bf82718ed},\n created = {2015-04-12T20:17:35.000Z},\n accessed = {2015-01-21},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The interface between terrestrial and aquatic ecosystems contributes to the provision of key ecosystem services including improved water quality and reduced flood risk. We develop an ecological–economic model using a Bayesian Belief Network (BBN) to assess and value the delivery of ecosystem services from riparian buffer strips. By capturing the interactions underlying ecosystem processes and the delivery of services we aim to further the operationalization of ecosystem services approaches. The model is developed through outlining the underlying ecological processes which deliver ecosystem services. Alternative management options and regional locations are used for sensitivity analysis. We identify optimal management options but reveal relatively small differences between impacts of different management options. We discuss key issues raised as a result of the probabilistic nature of the BBN model. Uncertainty over outcomes has implications for the approach to valuation particularly where preferences might exhibit non-linearities or thresholds. The interaction between probabilistic outcomes and the statistical nature of valuation estimates suggests the need for further exploration of sensitivity in such models. Although the BBN is a promising participatory decision support tool, there remains a need to understand the trade-off between realism, precision and the benefits of developing joint understanding of the decision context.},\n bibtype = {article},\n author = {McVittie, Alistair and Norton, Lisa and Martin-Ortega, Julia and Siameti, Ioanna and Glenk, Klaus and Aalders, Inge},\n doi = {10.1016/j.ecolecon.2014.12.004},\n journal = {Ecological Economics}\n}

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\n \n\n \n \n \n \n \n \n Consequence-based framework for electric power providers using Bayesian belief network.\n \n \n \n \n\n\n \n Buriticá, J., A.; and Tesfamariam, S.\n\n\n \n\n\n\n International Journal of Electrical Power & Energy Systems, 64: 233-241. 1 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Consequence-based$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Consequence-based framework for electric power providers using Bayesian belief network},\n type = {article},\n year = {2015},\n keywords = {Bayesian belief network,Consequence-based framework,Decision making,Power utility},\n pages = {233-241},\n volume = {64},\n websites = {http://www.sciencedirect.com/science/article/pii/S0142061514004669},\n month = {1},\n id = {dfe25f27-7a10-3631-a421-7f5eb56c3225},\n created = {2015-04-16T00:39:20.000Z},\n accessed = {2015-03-19},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Novel consequence-based framework for electric power providers is proposed. This framework includes six performance objectives, such as reputation, health and safety, environmental, financial, reliability, and system conditions. The six performance objectives are quantified with the consideration of 41 key performance indicators (KPIs). The framework is illustrated with a case study of 10 Canadian power utilities. Furthermore, a sensitivity analysis is undertaken to identify importance of the KPIs on the decision framework.},\n bibtype = {article},\n author = {Buriticá, Jessica A. and Tesfamariam, Solomon},\n doi = {10.1016/j.ijepes.2014.07.034},\n journal = {International Journal of Electrical Power & Energy Systems}\n}

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\n \n 2014\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n Modelling the benefits of habitat restoration in socio-ecological systems.\n \n \n \n \n\n\n \n Jellinek, S.; Rumpff, L.; Driscoll, D., A.; Parris, K., M.; and Wintle, B., A.\n\n\n \n\n\n\n Biological Conservation, 169: 60-67. 1 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Modelling$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Modelling the benefits of habitat restoration in socio-ecological systems},\n type = {article},\n year = {2014},\n keywords = {Bayesian Networks,Decision making,Expert opinion,Restoration,Revegetation,Socio-ecological systems,Species richness,Uncertainty},\n pages = {60-67},\n volume = {169},\n websites = {http://www.sciencedirect.com/science/article/pii/S0006320713003789},\n month = {1},\n id = {c24a8734-b3ea-3a33-81e1-d85aecde977b},\n created = {2015-04-12T18:51:30.000Z},\n accessed = {2015-01-22},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Decisions affecting the management of natural resources in agricultural landscapes are influenced by both social and ecological factors. Models that integrate these factors are likely to better predict the outcomes of natural resource management decisions compared to those that do not take these factors into account. We demonstrate how Bayesian Networks can be used to integrate ecological and social data and expert opinion to model the cost-effectiveness of revegetation activities for restoring biodiversity in agricultural landscapes. We demonstrate our approach with a case-study in grassy woodlands of south-eastern Australia. In our case-study, cost-effectiveness is defined as the improvement in native reptile and beetle species richness achieved per dollar spent on a restoration action. Socio-ecological models predict that weed control, the planting of trees and shrubs, the addition of litter and timber, and the addition of rocks are likely to be the most cost-effective actions for improving reptile and beetle species richness. The cost-effectiveness of restoration actions is lower in remnant and revegetated areas than in cleared areas because of the higher marginal benefits arising from acting in degraded habitats. This result is contingent on having favourable landowner attitudes. Under the best-case landowner demographic scenarios the greatest biodiversity benefits are seen when cleared areas are restored. We find that current restoration investment practices may not be increasing faunal species richness in agricultural landscapes in the most cost-effective way, and that new restoration actions may be necessary. Integrated socio-ecological models support transparent and cost-effective conservation investment decisions. Application of these models highlights the importance of collecting both social and ecological data when attempting to understand and manage socio-ecological systems.},\n bibtype = {article},\n author = {Jellinek, Sacha and Rumpff, Libby and Driscoll, Don A. and Parris, Kirsten M. and Wintle, Brendan A.},\n doi = {10.1016/j.biocon.2013.10.023},\n journal = {Biological Conservation}\n}

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\n \n\n \n \n \n \n \n \n Decision support from local data: creating adaptive order menus from past clinician behavior.\n \n \n \n \n\n\n \n Klann, J., G.; Szolovits, P.; Downs, S., M.; and Schadow, G.\n\n\n \n\n\n\n Journal of biomedical informatics, 48: 84-93. 4 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Decision$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Decision support from local data: creating adaptive order menus from past clinician behavior.},\n type = {article},\n year = {2014},\n keywords = {Algorithms,Ambulatory Care,Area Under Curve,Bayes Theorem,Computer Simulation,Data Mining,Data Mining: methods,Decision Making,Decision Support Systems, Clinical,Electronic Health Records,Evidence-Based Medicine,Female,Humans,Medical Records Systems, Computerized,Obstetrics,Obstetrics: methods,Pregnancy,Probability,Software,User-Computer Interface},\n pages = {84-93},\n volume = {48},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046413001962},\n month = {4},\n id = {095b6a05-3c38-3eb3-8405-d2b66bd48448},\n created = {2015-04-12T18:59:39.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {OBJECTIVE: Reducing care variability through guidelines has significantly benefited patients. Nonetheless, guideline-based Clinical Decision Support (CDS) systems are not widely implemented or used, are frequently out-of-date, and cannot address complex care for which guidelines do not exist. Here, we develop and evaluate a complementary approach - using Bayesian Network (BN) learning to generate adaptive, context-specific treatment menus based on local order-entry data. These menus can be used as a draft for expert review, in order to minimize development time for local decision support content. This is in keeping with the vision outlined in the US Health Information Technology Strategic Plan, which describes a healthcare system that learns from itself.\n\nMATERIALS AND METHODS: We used the Greedy Equivalence Search algorithm to learn four 50-node domain-specific BNs from 11,344 encounters: abdominal pain in the emergency department, inpatient pregnancy, hypertension in the Urgent Visit Clinic, and altered mental state in the intensive care unit. We developed a system to produce situation-specific, rank-ordered treatment menus from these networks. We evaluated this system with a hospital-simulation methodology and computed Area Under the Receiver-Operator Curve (AUC) and average menu position at time of selection. We also compared this system with a similar association-rule-mining approach.\n\nRESULTS: A short order menu on average contained the next order (weighted average length 3.91-5.83 items). Overall predictive ability was good: average AUC above 0.9 for 25% of order types and overall average AUC .714-.844 (depending on domain). However, AUC had high variance (.50-.99). Higher AUC correlated with tighter clusters and more connections in the graphs, indicating importance of appropriate contextual data. Comparison with an Association Rule Mining approach showed similar performance for only the most common orders with dramatic divergence as orders are less frequent.\n\nDISCUSSION AND CONCLUSION: This study demonstrates that local clinical knowledge can be extracted from treatment data for decision support. This approach is appealing because: it reflects local standards; it uses data already being captured; and it produces human-readable treatment-diagnosis networks that could be curated by a human expert to reduce workload in developing localized CDS content. The BN methodology captured transitive associations and co-varying relationships, which existing approaches do not. It also performs better as orders become less frequent and require more context. This system is a step forward in harnessing local, empirical data to enhance decision support.},\n bibtype = {article},\n author = {Klann, Jeffrey G and Szolovits, Peter and Downs, Stephen M and Schadow, Gunther},\n doi = {10.1016/j.jbi.2013.12.005},\n journal = {Journal of biomedical informatics}\n}

\n

\n\n\n

\n OBJECTIVE: Reducing care variability through guidelines has significantly benefited patients. Nonetheless, guideline-based Clinical Decision Support (CDS) systems are not widely implemented or used, are frequently out-of-date, and cannot address complex care for which guidelines do not exist. Here, we develop and evaluate a complementary approach - using Bayesian Network (BN) learning to generate adaptive, context-specific treatment menus based on local order-entry data. These menus can be used as a draft for expert review, in order to minimize development time for local decision support content. This is in keeping with the vision outlined in the US Health Information Technology Strategic Plan, which describes a healthcare system that learns from itself.\n\nMATERIALS AND METHODS: We used the Greedy Equivalence Search algorithm to learn four 50-node domain-specific BNs from 11,344 encounters: abdominal pain in the emergency department, inpatient pregnancy, hypertension in the Urgent Visit Clinic, and altered mental state in the intensive care unit. We developed a system to produce situation-specific, rank-ordered treatment menus from these networks. We evaluated this system with a hospital-simulation methodology and computed Area Under the Receiver-Operator Curve (AUC) and average menu position at time of selection. We also compared this system with a similar association-rule-mining approach.\n\nRESULTS: A short order menu on average contained the next order (weighted average length 3.91-5.83 items). Overall predictive ability was good: average AUC above 0.9 for 25% of order types and overall average AUC .714-.844 (depending on domain). However, AUC had high variance (.50-.99). Higher AUC correlated with tighter clusters and more connections in the graphs, indicating importance of appropriate contextual data. Comparison with an Association Rule Mining approach showed similar performance for only the most common orders with dramatic divergence as orders are less frequent.\n\nDISCUSSION AND CONCLUSION: This study demonstrates that local clinical knowledge can be extracted from treatment data for decision support. This approach is appealing because: it reflects local standards; it uses data already being captured; and it produces human-readable treatment-diagnosis networks that could be curated by a human expert to reduce workload in developing localized CDS content. The BN methodology captured transitive associations and co-varying relationships, which existing approaches do not. It also performs better as orders become less frequent and require more context. This system is a step forward in harnessing local, empirical data to enhance decision support.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Combining data and meta-analysis to build Bayesian networks for clinical decision support.\n \n \n \n \n\n\n \n Yet, B.; Perkins, Z., B.; Rasmussen, T., E.; Tai, N., R., M.; and Marsh, D., W., R.\n\n\n \n\n\n\n Journal of biomedical informatics, 52: 373-85. 12 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Combining$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Combining data and meta-analysis to build Bayesian networks for clinical decision support.},\n type = {article},\n year = {2014},\n keywords = {Bayesian networks,Clinical decision support,Evidence synthesis,Evidence-based medicine,Meta-analysis},\n pages = {373-85},\n volume = {52},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046414001816},\n month = {12},\n id = {96abb66e-d4e3-393f-bc24-fa6aedc745d3},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report 'univariate' relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model - ignoring some complexities of the problem - or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain.},\n bibtype = {article},\n author = {Yet, Barbaros and Perkins, Zane B and Rasmussen, Todd E and Tai, Nigel R M and Marsh, D William R},\n doi = {10.1016/j.jbi.2014.07.018},\n journal = {Journal of biomedical informatics}\n}

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\n \n\n \n \n \n \n \n \n A tool based on Bayesian networks for supporting geneticists in plant improvement by controlled pollination.\n \n \n \n \n\n\n \n Nielsen, J., D.; Salmerón, A.; and Gámez, J., A.\n\n\n \n\n\n\n International Journal of Approximate Reasoning, 55(1): 74-83. 1 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A tool based on Bayesian networks for supporting geneticists in plant improvement by controlled pollination},\n type = {article},\n year = {2014},\n keywords = {Bayesian networks,Decision support systems,Inference,Learning,Vegetal genetic improvement},\n pages = {74-83},\n volume = {55},\n websites = {http://www.sciencedirect.com/science/article/pii/S0888613X13000704},\n month = {1},\n id = {b1ce2b97-4f12-334d-ae53-cf4dedd210b8},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In this paper we describe a system designed for assisting geneticists in vegetal genetic improvement tasks. The system is based on the use of Bayesian networks. It has been developed under the industrial demands emerging from the area of Campo de Dalías in Almería (Spain), and is therefore oriented to producing new tomato varieties, which constitute the main product in the area. The paper concentrates on the main aspects of the design of the system.},\n bibtype = {article},\n author = {Nielsen, Jens D. and Salmerón, Antonio and Gámez, José A.},\n doi = {10.1016/j.ijar.2013.03.010},\n journal = {International Journal of Approximate Reasoning},\n number = {1}\n}

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\n \n 2013\n \n \n (7)\n \n \n

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\n \n\n \n \n \n \n \n \n Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring.\n \n \n \n \n\n\n \n Bulu, H.; Alpkocak, A.; and Balci, P.\n\n\n \n\n\n\n Computers in biology and medicine, 43(4): 301-11. 5 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncertainty$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring.},\n type = {article},\n year = {2013},\n keywords = {Algorithms,Bayes Theorem,Breast,Breast Neoplasms,Breast Neoplasms: diagnosis,Breast: pathology,Calcinosis,Calcinosis: pathology,Computer Simulation,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Female,Humans,Mammography,Mammography: methods,Models, Statistical,Programming Languages,Uncertainty,User-Computer Interface},\n pages = {301-11},\n volume = {43},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482513000139},\n month = {5},\n id = {db2aa74c-81d4-3a24-b00a-8d96fe1212d3},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper presents an ontology-based annotation system and BI-RADS (Breast Imaging Reporting and Data System) score reasoning with Semantic Web technologies in mammography. The annotation system is based on the Mammography Annotation Ontology (MAO) where the BI-RADS score reasoning works. However, ontologies are based on crisp logic and they cannot handle uncertainty. Consequently, we propose a Bayesian-based approach to model uncertainty in mammography ontology and make reasoning possible using BI-RADS scores with SQWRL (Semantic Query-enhanced Web Rule Language). First, we give general information about our system and present details of mammography annotation ontology, its main concepts and relationships. Then, we express uncertainty in mammography and present approaches to handle uncertainty issues. System is evaluated with a manually annotated dataset DEMS (Dokuz Eylul University Mammography Set) and DDSM (Digital Database for Screening Mammography). We give the result of experimentations in terms of accuracy, sensitivity, precision and uncertainty level measures.},\n bibtype = {article},\n author = {Bulu, Hakan and Alpkocak, Adil and Balci, Pinar},\n doi = {10.1016/j.compbiomed.2013.01.001},\n journal = {Computers in biology and medicine},\n number = {4}\n}

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\n \n\n \n \n \n \n \n \n Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment).\n \n \n \n \n\n\n \n Keshtkar, A.; Salajegheh, A.; Sadoddin, A.; and Allan, M.\n\n\n \n\n\n\n Ecological Modelling, 268: 48-54. 10 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Application$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment)},\n type = {article},\n year = {2013},\n keywords = {Bayesian network,Decision support,Integrated catchment management,Iran,Sustainability assessment,Vegetation management},\n pages = {48-54},\n volume = {268},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380013003967},\n month = {10},\n id = {0ac207fc-7ec9-347a-8d12-71b188752f74},\n created = {2015-04-12T18:59:39.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Catchment management is a process which increases the sustainable development and management of all catchment resources in order to maximize the balance among socioeconomic welfare and the sustainability of vital ecosystems. The increase of anthropogenic activities within river catchments causes degradation and serious problems for stakeholders and managers, particularly in arid and semi-arid regions. Although there are many techniques for solving these problems, it is not easy for catchment managers to apply them. An integrated Bayesian network model framework was applied to evaluate the sustainability of a semi-arid river catchment located in the Iranian Central Plateau river basin encompassing 32.6km2 area on the Hablehrood river catchment, located in the northern part of the Iranian Central Plateau river basin. The research illustrated the assessment of the relevant management problems, the model framework, and the techniques applied to extract input data. Results for the study area implementation and a suggestion for management are described and discussed.},\n bibtype = {article},\n author = {Keshtkar, A.R. and Salajegheh, A. and Sadoddin, A. and Allan, M.G.},\n doi = {10.1016/j.ecolmodel.2013.08.003},\n journal = {Ecological Modelling}\n}

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\n \n\n \n \n \n \n \n \n An autonomous mobile system for the management of COPD.\n \n \n \n \n\n\n \n van der Heijden, M.; Lucas, P., J., F.; Lijnse, B.; Heijdra, Y., F.; and Schermer, T., R., J.\n\n\n \n\n\n\n Journal of biomedical informatics, 46(3): 458-69. 6 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {An autonomous mobile system for the management of COPD.},\n type = {article},\n year = {2013},\n keywords = {Artificial Intelligence,Computer Security,Disease Management,Feasibility Studies,Humans,Internet,Models, Theoretical,Pilot Projects,Probability,Pulmonary Disease, Chronic Obstructive,Pulmonary Disease, Chronic Obstructive: therapy,ROC Curve,Telemedicine},\n pages = {458-69},\n volume = {46},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046413000373},\n month = {6},\n id = {c80d7b96-fcc4-3d89-912c-848bbb28be8d},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {INTRODUCTION: Managing chronic disease through automated systems has the potential to both benefit the patient and reduce health-care costs. We have developed and evaluated a disease management system for patients with chronic obstructive pulmonary disease (COPD). Its aim is to predict and detect exacerbations and, through this, help patients self-manage their disease to prevent hospitalisation.\n\nMATERIALS: The carefully crafted intelligent system consists of a mobile device that is able to collect case-specific, subjective and objective, physiological data, and to alert the patient by a patient-specific interpretation of the data by means of probabilistic reasoning. Collected data are also sent to a central server for inspection by health-care professionals.\n\nMETHODS: We evaluated the probabilistic model using cross-validation and ROC analyses on data from an earlier study and by an independent data set. Furthermore a pilot with actual COPD patients has been conducted to test technical feasibility and to obtain user feedback.\n\nRESULTS: Model evaluation results show that we can reliably detect exacerbations. Pilot study results suggest that an intervention based on this system could be successful.},\n bibtype = {article},\n author = {van der Heijden, Maarten and Lucas, Peter J F and Lijnse, Bas and Heijdra, Yvonne F and Schermer, Tjard R J},\n doi = {10.1016/j.jbi.2013.03.003},\n journal = {Journal of biomedical informatics},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Mining monitored data for decision-making with a Bayesian network model.\n \n \n \n \n\n\n \n Williams, B.; and Cole, B.\n\n\n \n\n\n\n Ecological Modelling, 249: 26-36. 1 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Mining$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Mining monitored data for decision-making with a Bayesian network model},\n type = {article},\n year = {2013},\n keywords = {Bayesian networks,Cyanobacteria,Data mining,Elicitation,Reservoir management,Water quality},\n pages = {26-36},\n volume = {249},\n websites = {http://www.sciencedirect.com/science/article/pii/S030438001200333X},\n month = {1},\n id = {5fab619e-34f7-3a3a-b4e3-1d0c427566d7},\n created = {2015-04-12T20:17:34.000Z},\n accessed = {2015-04-08},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {A Bayesian network model of Anabaena blooms in Grahamstown Dam, near Newcastle, Australia is described. This model meets the criteria of being decision-focused, data driven, transparent, and capable of being used by non-expert modellers. Monitored data were arranged in a consistently formatted database from which the model could ‘learn’ probabilistic relationships between model elements such as pumped nutrient load, lake water column nutrient concentrations, and Anabaena concentrations. This ‘minimal model’ produced useful insights into ecosystem relationships and provided a basic model for later development. Subsequent modelling and elicitation of conditional probabilities from experts strengthened components of the model for which there is little data available. The approach to incorporating elicited data is described and some simple scenario testing is also presented. Management outcomes resulting from application of the model are presented.},\n bibtype = {article},\n author = {Williams, B.J. and Cole, B.},\n doi = {10.1016/j.ecolmodel.2012.07.008},\n journal = {Ecological Modelling}\n}

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\n \n\n \n \n \n \n \n \n Dynamic decision making for graphical models applied to oil exploration.\n \n \n \n \n\n\n \n Martinelli, G.; Eidsvik, J.; and Hauge, R.\n\n\n \n\n\n\n European Journal of Operational Research, 230(3): 688-702. 11 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Dynamic$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Dynamic decision making for graphical models applied to oil exploration},\n type = {article},\n year = {2013},\n keywords = {Bayesian Networks,Dynamic programming,Graphical model,Heuristics,Petroleum exploration},\n pages = {688-702},\n volume = {230},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221713003834},\n month = {11},\n id = {b00e6862-b69c-3b97-ab5a-44cf5496a6e0},\n created = {2018-03-31T22:35:56.263Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2018-03-31T22:35:56.263Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We present a framework for sequential decision making in problems described by graphical models. The setting is given by dependent discrete random variables with associated costs or revenues. In our examples, the dependent variables are the potential outcomes (oil, gas or dry) when drilling a petroleum well. The goal is to develop an optimal selection strategy of wells that incorporates a chosen utility function within an approximated dynamic programming scheme. We propose and compare different approximations, from naive and myopic heuristics to more complex look-ahead schemes, and we discuss their computational properties. We apply these strategies to oil exploration over multiple prospects modeled by a directed acyclic graph, and to a reservoir drilling decision problem modeled by a Markov random field. The results show that the suggested strategies clearly improve the naive or myopic constructions used in petroleum industry today. This is useful for decision makers planning petroleum exploration policies.},\n bibtype = {article},\n author = {Martinelli, Gabriele and Eidsvik, Jo and Hauge, Ragnar},\n doi = {10.1016/j.ejor.2013.04.057},\n journal = {European Journal of Operational Research},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Decision support system for Warfarin therapy management using Bayesian networks.\n \n \n \n \n\n\n \n Yet, B.; Bastani, K.; Raharjo, H.; Lifvergren, S.; Marsh, W.; and Bergman, B.\n\n\n \n\n\n\n Decision Support Systems, 55(2): 488-498. 5 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Decision$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Decision support system for Warfarin therapy management using Bayesian networks},\n type = {article},\n year = {2013},\n keywords = {Anticoagulant therapy,Bayesian networks,Decision support systems,Warfarin therapy},\n pages = {488-498},\n volume = {55},\n websites = {http://www.sciencedirect.com/science/article/pii/S016792361200262X},\n month = {5},\n id = {de6ef649-c26c-3786-b436-b3b52eea3f17},\n created = {2018-03-31T22:35:56.264Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2018-03-31T22:35:56.264Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Warfarin therapy is known as a complex process because of the variation in the patients' response. Failure to deal with such variation may lead to death as a result of thrombosis or bleeding. The possible sources of variation such as concomitant illnesses and drug interactions have to be investigated by the clinician in order to deal with the variation. This paper describes a decision support system (DSS) using Bayesian networks for assisting clinicians to make better decisions in Warfarin therapy management. The DSS is developed in collaboration with a Swedish hospital group that manages Warfarin therapy for more than 3000 patients. The proposed model can assist the clinician in making dose-adjustment and follow-up interval decisions, investigating variation causes, and evaluating bleeding and thrombosis risks related to therapy. The model is built upon previous findings from medical literature, the knowledge of domain experts, and large dataset of patients.},\n bibtype = {article},\n author = {Yet, Barbaros and Bastani, Kaveh and Raharjo, Hendry and Lifvergren, Svante and Marsh, William and Bergman, Bo},\n doi = {10.1016/j.dss.2012.10.007},\n journal = {Decision Support Systems},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Bayesian networks for clinical decision support in lung cancer care.\n \n \n \n \n\n\n \n Berkan Sesen, M.; Nicholson, A., E.; Banares-Alcantara, R.; Kadir, T.; and Brady, M.\n\n\n \n\n\n\n PLoS ONE, 8(12). 12 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Bayesian$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Bayesian networks for clinical decision support in lung cancer care},\n type = {article},\n year = {2013},\n volume = {8},\n month = {12},\n publisher = {Public Library of Science},\n day = {6},\n id = {d518f9d0-dc25-3931-bc81-72d4858c3da2},\n created = {2020-02-20T19:55:51.976Z},\n accessed = {2020-02-20},\n file_attached = {true},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2020-02-20T19:55:52.070Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Survival prediction and treatment selection in lung cancer care are characterised by high levels of uncertainty. Bayesian Networks (BNs), which naturally reason with uncertain domain knowledge, can be applied to aid lung cancer experts by providing personalised survival estimates and treatment selection recommendations. Based on the English Lung Cancer Database (LUCADA), we evaluate the feasibility of BNs for these two tasks, while comparing the performances of various causal discovery approaches to uncover the most feasible network structure from expert knowledge and data. We show first that the BN structure elicited from clinicians achieves a disappointing area under the ROC curve of 0.75 (± 0.03), whereas a structure learned by the CAMML hybrid causal discovery algorithm, which adheres with the temporal restrictions, achieves 0.81 (± 0.03). Second, our causal intervention results reveal that BN treatment recommendations, based on prescribing the treatment plan that maximises survival, can only predict the recorded treatment plan 29% of the time. However, this percentage rises to 76% when partial matches are included. © 2013 Sesen et al.},\n bibtype = {article},\n author = {Berkan Sesen, M. and Nicholson, Ann E. and Banares-Alcantara, Rene and Kadir, Timor and Brady, Michael},\n doi = {10.1371/journal.pone.0082349},\n journal = {PLoS ONE},\n number = {12}\n}

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\n \n 2012\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n A Bayesian network approach for selecting translocation sites for endangered island birds.\n \n \n \n \n\n\n \n Laws, R., J.; and Kesler, D., C.\n\n\n \n\n\n\n Biological Conservation, 155: 178-185. 10 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian network approach for selecting translocation sites for endangered island birds},\n type = {article},\n year = {2012},\n keywords = {Assisted colonization,Bayesian network models,Island bird conservation,Island conservation,Micronesian kingfishers,Site selection,Translocation},\n pages = {178-185},\n volume = {155},\n websites = {http://www.sciencedirect.com/science/article/pii/S0006320712002650},\n month = {10},\n id = {b74bf493-51b0-3608-b4e0-a13d7b6e9c8a},\n created = {2015-04-12T19:14:39.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Translocation has become increasingly important for conserving island species. Limited tools are available for guiding the selection of translocation sites, however, particularly when establishing rescue populations outside of historic ranges. We developed a Bayesian network model framework for translocation site selection for island birds. The model consisted of four primary components including ecological requirements for survival, anthropogenic threats at the population establishment site, effects the translocated species may have on native species, and operational support associated with the translocation process and ongoing management. We then used the model to identify potential sites for the establishment of a wild population of Guam Micronesian kingfishers (Todiramphus cinnamominus cinnamominus) on an island outside the bird’s historic range. Conditional probabilities that guided model evaluations were allocated using information from the literature, expert opinions, and a training set of islands outside the region under consideration for releases. The model was used to evaluate 239 islands where a translocation population of Micronesian kingfishers could be established. Five islands, all in the Federated States of Micronesia, were identified as being suitable for assisted colonization, including Kosrae, Yap, Faichuk, Weno and Fefan. Sensitivity analysis showed a correspondence between model variables and island characteristics indicated by the literature as being the most important for successful translocation. We found the Bayesian network model to be a useful tool for translocation site selection despite limited information on the natural history of the Guam Micronesian kingfisher and the factors that impact the success of translocations.},\n bibtype = {article},\n author = {Laws, Rebecca J. and Kesler, Dylan C.},\n doi = {10.1016/j.biocon.2012.05.016},\n journal = {Biological Conservation}\n}

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\n \n\n \n \n \n \n \n \n Bayesian networks and the quest for reserve adequacy.\n \n \n \n \n\n\n \n Schapaugh, A., W.; and Tyre, A., J.\n\n\n \n\n\n\n Biological Conservation, 152: 178-186. 8 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Bayesian$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Bayesian networks and the quest for reserve adequacy},\n type = {article},\n year = {2012},\n keywords = {Bayesian network,Interior least tern,Persistence,Piping plover,Reserve adequacy,Reserve selection,Stochastic dynamic programming,Whooping crane},\n pages = {178-186},\n volume = {152},\n websites = {http://www.sciencedirect.com/science/article/pii/S0006320712001577},\n month = {8},\n id = {75ea87b6-d6c4-3e97-98de-432d8d29e7b4},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The fundamental goal of conservation planning is biodiversity persistence, yet most reserve selection methods prioritize sites using occurrence data. We describe a method that integrates correlates of persistence for multiple species into a single currency – site quality. Site quality is, in turn, an explicit measure of performance used in optimization. We develop a Bayesian network to assess site quality, which assigns an expected value to a property based on criteria arrayed into a causal diagram. We then use stochastic dynamic programming to determine whether an organization should acquire or reject a site placed on the public market. Our framework for assessing sites and making land acquisition decisions represents a compromise between the use of generic spatial design criteria and more intensive computational tools, like spatially-explicit population models. There is certainly a loss of precision by using site quality as a surrogate for more direct measures of persistence. However, we believe this simplification is defensible when sufficient data, expertise, or other resources are lacking.},\n bibtype = {article},\n author = {Schapaugh, Adam W. and Tyre, Andrew J.},\n doi = {10.1016/j.biocon.2012.03.014},\n journal = {Biological Conservation}\n}

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\n \n 2011\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n State-and-transition modelling for Adaptive Management of native woodlands.\n \n \n \n \n\n\n \n Rumpff, L.; Duncan, D.; Vesk, P.; Keith, D.; and Wintle, B.\n\n\n \n\n\n\n Biological Conservation, 144(4): 1224-1236. 4 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"State-and-transition$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {State-and-transition modelling for Adaptive Management of native woodlands},\n type = {article},\n year = {2011},\n keywords = {Adaptive Management,Bayesian network,Native vegetation,Process model,Restoration,State-and-transition},\n pages = {1224-1236},\n volume = {144},\n websites = {http://www.sciencedirect.com/science/article/pii/S0006320710004763},\n month = {4},\n id = {2b11c640-61d1-39c1-861e-25062fdf74f6},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Adaptive Management (AM) is widely advocated as an approach to dealing with uncertainty in natural resource management as it provides an explicit framework for motivating, designing and interpreting the results of monitoring. One of the major factors impeding implementation is the failure to use appropriate process models; a core element of AM. Process models represent beliefs about the properties and dynamics of an ecological system and ecosystem responses to management. Quantitative models of ecosystem response help resolve ambiguity about the efficacy of management and facilitate iterative updating of knowledge using monitoring data. We report on the use of a state-and-transition model (STM) in the Adaptive Management of native woodland vegetation in south-eastern Australia. The STM is implemented as a Bayesian network, making it simple to communicate and update with new data as they arise. Application of the model is demonstrated using case-study and simulation data. We show how the model may be used to predict the probability of achieving desirable state transitions at restoration sites and how monitoring of those sites can be used to update the model (learn) and adapt (review restoration strategies). After just one monitoring/learning cycle, 7years after the first investments, we found that updated models predict markedly different transition probabilities compared with initial models based on expert opinion. This has strong implications for the apparent cost-efficiency of restoration strategies. The STM approach provides a sound theoretical basis for restoration decisions, while the Bayesian network implementation provides a workable framework for using the STM adaptively.},\n bibtype = {article},\n author = {Rumpff, L. and Duncan, D.H. and Vesk, P.A. and Keith, D.A. and Wintle, B.A.},\n doi = {10.1016/j.biocon.2010.10.026},\n journal = {Biological Conservation},\n number = {4}\n}

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\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Combining state and transition models with dynamic Bayesian networks.\n \n \n \n \n\n\n \n Nicholson, A., E.; and Flores, M., J.\n\n\n \n\n\n\n Ecological Modelling, 222(3): 555-566. 2 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Combining$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Combining state and transition models with dynamic Bayesian networks},\n type = {article},\n year = {2011},\n keywords = {Bayesian networks,Dynamic Bayesian networks,Rangeland management,State-and-transition models,System dynamics},\n pages = {555-566},\n volume = {222},\n websites = {http://www.sciencedirect.com/science/article/pii/S030438001000551X},\n month = {2},\n id = {f9797d11-e29e-3e67-a0a1-55dbf88b1e96},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Bashari et al. (2009) propose combining state and transition models (STMs) with Bayesian networks for decision support tools where the focus is on modelling the system dynamics. There is already an extension of Bayesian networks – so-called dynamic Bayesian networks (DBNs) – for explicitly modelling systems that change over time, that has also been applied in ecological modelling. In this paper we propose a combination of STMs and DBNs that overcome some of the limitations of Bashari et al.’s approach including providing an explicit representation of the next state, while retaining its advantages, such an the explicit representation of transitions. We then show that the new model can be applied iteratively to predict into the future consistently with different time frames. We use Bashari et al.’s rangeland management problem as an illustrative case study. We present a comparative complexity analysis of the different approaches, based on the structure inherent in the problem being modelled. This analysis showed that any models that explicitly represent all the transitions only remain tractable when there are natural constraints in the domain. Thus we recommend modellers should analyse these aspects of their problem before deciding whether to use the framework.},\n bibtype = {article},\n author = {Nicholson, Ann E. and Flores, M. Julia},\n doi = {10.1016/j.ecolmodel.2010.10.010},\n journal = {Ecological Modelling},\n number = {3}\n}

\n

\n\n\n\n\n\n

\n

\n \n 2010\n \n \n (1)\n \n \n

\n

\n \n \n

\n \n\n \n \n \n \n \n \n Using hierarchical dynamic Bayesian networks to investigate dynamics of organ failure in patients in the Intensive Care Unit.\n \n \n \n \n\n\n \n Peelen, L.; de Keizer, N., F.; Jonge, E., d.; Bosman, R.; Abu-Hanna, A.; and Peek, N.\n\n\n \n\n\n\n Journal of biomedical informatics, 43(2): 273-86. 4 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Using hierarchical dynamic Bayesian networks to investigate dynamics of organ failure in patients in the Intensive Care Unit.},\n type = {article},\n year = {2010},\n keywords = {Bayes Theorem,Computational Biology,Computational Biology: methods,Female,Humans,Intensive Care Units,Intensive Care Units: statistics & numerical data,Logistic Models,Male,Markov Chains,Multiple Organ Failure,Multiple Organ Failure: epidemiology,Predictive Value of Tests,Prognosis,Prospective Studies,Sepsis,Sepsis: diagnosis,Sepsis: mortality,Time Factors},\n pages = {273-86},\n volume = {43},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046409001373},\n month = {4},\n id = {4131acba-a4bb-3044-828b-0053c2539262},\n created = {2015-04-12T20:17:36.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In intensive care medicine close monitoring of organ failure status is important for the prognosis of patients and for choices regarding ICU management. Major challenges in analyzing the multitude of data pertaining to the functioning of the organ systems over time are to extract meaningful clinical patterns and to provide predictions for the future course of diseases. With their explicit states and probabilistic state transitions, Markov models seem to fit this purpose well. In complex domains such as intensive care a choice is often made between a simple model that is estimated from the data, or a more complex model in which the parameters are provided by domain experts. Our primary aim is to combine these approaches and develop a set of complex Markov models based on clinical data. In this paper we describe the design choices underlying the models, which enable them to identify temporal patterns, predict outcomes, and test clinical hypotheses. Our models are characterized by the choice of the dynamic hierarchical Bayesian network structure and the use of logistic regression equations in estimating the transition probabilities. We demonstrate the induction, inference, evaluation, and use of these models in practice in a case-study of patients with severe sepsis admitted to four Dutch ICUs.},\n bibtype = {article},\n author = {Peelen, Linda and de Keizer, Nicolette F and Jonge, Evert de and Bosman, Robert-Jan and Abu-Hanna, Ameen and Peek, Niels},\n doi = {10.1016/j.jbi.2009.10.002},\n journal = {Journal of biomedical informatics},\n number = {2}\n}

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\n \n 2008\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n Evolution and challenges in the design of computational systems for triage assistance.\n \n \n \n \n\n\n \n Abad-Grau, M., M.; Ierache, J.; Cervino, C.; and Sebastiani, P.\n\n\n \n\n\n\n Journal of biomedical informatics, 41(3): 432-41. 6 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Evolution$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Evolution and challenges in the design of computational systems for triage assistance.},\n type = {article},\n year = {2008},\n keywords = {Artificial Intelligence,Decision Support Systems, Clinical,Decision Support Systems, Clinical: organization &,Software,Software Design,Triage,Triage: methods,Triage: organization & administration},\n pages = {432-41},\n volume = {41},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046408000130},\n month = {6},\n id = {d8dfcab5-eab4-3277-b191-2ed084b7cd66},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Compared with expert systems for specific disease diagnosis, knowledge-based systems to assist decision making in triage usually try to cover a much wider domain but can use a smaller set of variables due to time restrictions, many of them subjective so that accurate models are difficult to build. In this paper, we first study criteria that most affect the performance of systems for triage assistance. Such criteria include whether principled approaches from machine learning can be used to increase accuracy and robustness and to represent uncertainty, whether data and model integration can be performed or whether temporal evolution can be modeled to implement retriage or represent medication responses. Following the most important criteria, we explore current systems and identify some missing features that, if added, may yield to more accurate triage systems.},\n bibtype = {article},\n author = {Abad-Grau, María M and Ierache, Jorge and Cervino, Claudio and Sebastiani, Paola},\n doi = {10.1016/j.jbi.2008.01.007},\n journal = {Journal of biomedical informatics},\n number = {3}\n}

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\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Exploiting missing clinical data in Bayesian network modeling for predicting medical problems.\n \n \n \n \n\n\n \n Lin, J.; and Haug, P., J.\n\n\n \n\n\n\n Journal of biomedical informatics, 41(1): 1-14. 2 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Exploiting$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Exploiting missing clinical data in Bayesian network modeling for predicting medical problems.},\n type = {article},\n year = {2008},\n keywords = {Artificial Intelligence,Bayes Theorem,Database Management Systems,Decision Support Systems, Clinical,Decision Support Techniques,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Information Storage and Retrieval,Information Storage and Retrieval: methods,Medical Records Systems, Computerized,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Risk Assessment,Risk Assessment: methods,Risk Factors},\n pages = {1-14},\n volume = {41},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046407000524},\n month = {2},\n id = {80376bc0-60ef-3446-b8d8-fa3c849a1d73},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {When machine learning algorithms are applied to data collected during the course of clinical care, it is generally accepted that the data has not been consistently collected. The absence of expected data elements is common and the mechanism through which a data element is missing often involves the clinical relevance of that data element in a specific patient. Therefore, the absence of data may have information value of its own. In the process of designing an application intended to support a medical problem list, we have studied whether the "missingness" of clinical data can provide useful information in building prediction models. In this study, we experimented with four methods of treating missing values in a clinical data set-two of them explicitly model the absence or "missingness" of data. Each of these data sets were used to build four different kinds of Bayesian classifiers-a naive Bayes structure, a human-composed network structure, and two networks based on structural learning algorithms. We compared the performance between groups with and without explicit models of missingness using the area under the ROC curve. The results showed that in most cases the classifiers trained using the explicit missing value treatments performed better. The result suggests that information may exist in "missingness" itself. Thus, when designing a decision support system, we suggest one consider explicitly representing the presence/absence of data in the underlying logic.},\n bibtype = {article},\n author = {Lin, Jau-Huei and Haug, Peter J},\n doi = {10.1016/j.jbi.2007.06.001},\n journal = {Journal of biomedical informatics},\n number = {1}\n}

\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Monitoring amphibian populations with incomplete survey information using a Bayesian probabilistic model.\n \n \n \n \n\n\n \n Wilson, D., S.; Stoddard, M., A.; and Puettmann, K., J.\n\n\n \n\n\n\n Ecological Modelling, 214(2-4): 210-218. 6 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Monitoring amphibian populations with incomplete survey information using a Bayesian probabilistic model},\n type = {article},\n year = {2008},\n keywords = {Amphibian monitoring,Ascaphus truei,Bayesian networks,Dicamptodon tenebrosus,Hierarchical Bayesian models,Rhyacotriton spp.},\n pages = {210-218},\n volume = {214},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380008000768},\n month = {6},\n id = {c7242726-a6d8-3c28-a33a-bcd754e4dcfe},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Bayesian networks (BNs) are a probabilistic modeling platform that connect variables through a series of conditional dependences. We demonstrate their utility for broad-scale conservation of amphibian populations where different types of information may be available within the region. Wildlife conservation decisions for most species are made jointly with other objectives and are tightly constrained by finances. Bayesian networks allow the use of all available information in predictions, and can provide managers with the best available information for making decisions. Habitat models were developed as a hierarchical Bayesian (HB) model for aquatic amphibian populations in the temperate Oregon Coast Range, USA. Predictions for new streams sections were made jointly using a Bayesian network to allow the inclusion of different types of available information. Missing habitat variables were modeled based on habitat survey information. Uncertainty in the true (but unknown) habitat variables were incorporated into the prediction intervals. Further, the probabilistic approach allowed us to incorporate survey information for co-occurring species to help make better predictions. Such species information was connected through the Bayesian network by the conditional dependence that arises from shared habitat variables. The utility of Bayesian networks was shown for these populations for broad-scale risk management. In contrast to deterministic models, the probabilistic nature of Bayesian networks is a natural platform for incorporating uncertainty in predictions and inference.},\n bibtype = {article},\n author = {Wilson, Duncan S. and Stoddard, Margo A. and Puettmann, Klaus J.},\n doi = {10.1016/j.ecolmodel.2008.02.003},\n journal = {Ecological Modelling},\n number = {2-4}\n}

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\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities.\n \n \n \n\n\n \n Gupta, S.; and Kim, H., W.\n\n\n \n\n\n\n European Journal of Operational Research, 190(3): 818-833. 11 2008.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities},\n type = {article},\n year = {2008},\n keywords = {Bayesian networks,Customer retention,Decision support,Structural equation modeling,Virtual community},\n pages = {818-833},\n volume = {190},\n month = {11},\n day = {1},\n id = {58ca83a4-0a35-303c-b429-cff40b2781ad},\n created = {2020-01-06T20:25:46.115Z},\n accessed = {2020-01-06},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2020-01-06T20:25:46.235Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Bayesian networks are limited in differentiating between causal and spurious relationships among decision factors. Decision making without differentiating the two relationships cannot be effective. To overcome this limitation of Bayesian networks, this study proposes linking Bayesian networks to structural equation modeling (SEM), which has an advantage in testing causal relationships between factors. The capability of SEM in empirical validation combined with the prediction and diagnosis capabilities of Bayesian modeling facilitates effective decision making from identification of causal relationships to decision support. This study applies the proposed integrated approach to decision support for customer retention in a virtual community. The application results provide insights for practitioners on how to retain their customers. This research benefits Bayesian researchers by providing the application of modeling causal relationships at latent variable level, and helps SEM researchers in extending their models for managerial prediction and diagnosis. © 2007 Elsevier B.V. All rights reserved.},\n bibtype = {article},\n author = {Gupta, Sumeet and Kim, Hee W.},\n doi = {10.1016/j.ejor.2007.05.054},\n journal = {European Journal of Operational Research},\n number = {3}\n}

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\n\n\n\n\n\n

\n

\n \n 2007\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n Examination of conflicts and improved strategies for the management of an endangered Eucalypt species using Bayesian networks.\n \n \n \n \n\n\n \n Pollino, C., A.; White, A., K.; and Hart, B., T.\n\n\n \n\n\n\n Ecological Modelling, 201(1): 37-59. 2 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"Examination$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Examination of conflicts and improved strategies for the management of an endangered Eucalypt species using Bayesian networks},\n type = {article},\n year = {2007},\n keywords = {Bayesian networks,Endangered species,Hypotheses},\n pages = {37-59},\n volume = {201},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380006003541},\n month = {2},\n id = {8358f9d1-e915-3535-8bb9-98222e1be4b2},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Bayesian decision support tools are becoming increasingly popular as a modelling framework that can analyse complex problems, resolve controversies, and support future decision-making in an adaptive management framework. This paper introduces a model designed to assist the management of an endangered Eucalypt species, the Swamp Gum (Eucalyptus camphora). This tree species is found in the Yellingbo Nature Conservation Reserve (YNCR), an isolated patch of forest in the Yarra Valley (Victoria, Australia), where E. camphora has become increasingly threatened by dieback. In order to maintain and rehabilitate existing trees and encourage regeneration, management strategies and action plans have concentrated on restoring the hydrological regime, which has been altered due to agricultural activities within the catchment. However, research suggests that nutrient enrichment from surrounding horticulture and livestock is having a greater impact on the health of the trees. A Bayesian network model has been developed for E. camphora and used to explore the differences between these two hypotheses. Model outputs suggest that the influencing factors of E. camphora condition are (a) spatially specific and (b) differ according to the group conducting the study in the YNCR. Given the poor quality of data and knowledge available, further research is required to identify the causal factors of dieback. The model offers a framework to guide future integrative and iterative monitoring and research in the YNCR.},\n bibtype = {article},\n author = {Pollino, Carmel A. and White, Andrea K. and Hart, Barry T.},\n doi = {10.1016/j.ecolmodel.2006.07.032},\n journal = {Ecological Modelling},\n number = {1}\n}

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\n \n 2006\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n Using hidden multi-state Markov models with multi-parameter volcanic data to provide empirical evidence for alert level decision-support.\n \n \n \n \n\n\n \n Aspinall, W.; Carniel, R.; Jaquet, O.; Woo, G.; and Hincks, T.\n\n\n \n\n\n\n Journal of Volcanology and Geothermal Research, 153(1-2): 112-124. 5 2006.\n \n\n\n\n
\n\n\n\n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@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 = {205bfc10-4c24-358c-a686-beddc7e030bc},\n created = {2015-04-12T21:20:17.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\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}

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\n \n 2003\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n Evidence-based volcanology: application to eruption crises.\n \n \n \n \n\n\n \n Aspinall, W.; Woo, G.; Voight, B.; and Baxter, P.\n\n\n \n\n\n\n Journal of Volcanology and Geothermal Research, 128(1-3): 273-285. 11 2003.\n \n\n\n\n
\n\n\n\n \n \n $\"Evidence-based$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Evidence-based volcanology: application to eruption crises},\n type = {article},\n year = {2003},\n keywords = {Bayesian Belief Network,Bayes’ Rule,Evidence Science,Galeras volcano,decision support,expert judgment,risk assessment,volcanic eruption},\n pages = {273-285},\n volume = {128},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377027303002609},\n month = {11},\n id = {6256dd67-cc73-3125-bf4d-2a066174f171},\n created = {2015-04-12T21:20:17.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The way in which strands of uncertain volcanological evidence can be used for decision-making, and the weight that should be given them, is a problem requiring formulation in terms of the logical principles of Evidence Science. The basic ideas are outlined using the explosion at Galeras volcano in Colombia in January 1993 as an example. Our retrospective analysis suggests that if a robust precautionary appraisal had been made of the circumstances in which distinctive tornillo signals were detected at Galeras, those events might have been construed as stronger precursory evidence for imminent explosive activity than were the indications for quiescence, given by the absence of other warning traits. However, whilst visits to the crater might have been recognised as involving elevated risk if this form of analysis had been applied to the situation in January 1993, a traditional scientific consideration of the available information was likely to have provided a neutral assessment of short-term risk levels. We use these inferences not to criticise interpretations or decisions made at the time, but to illustrate how a structured, evidence-based analysis procedure might have provided a different perspective to that derived from the conventional scientific standpoint. We advocate a formalism that may aid such decision-making in future: graphical Bayesian Belief Networks are introduced as a tool for performing the necessary numerical procedures. With this approach, Evidence Science concepts can be incorporated rationally, efficiently and reliably into decision support during volcanic crises.},\n bibtype = {article},\n author = {Aspinall, W.P. and Woo, G. and Voight, B. and Baxter, P.J.},\n doi = {10.1016/S0377-0273(03)00260-9},\n journal = {Journal of Volcanology and Geothermal Research},\n number = {1-3}\n}

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\n \n 1999\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n Application of belief networks to water management studies.\n \n \n \n \n\n\n \n Batchelor, C.; and Cain, J.\n\n\n \n\n\n\n Agricultural Water Management, 40(1): 51-57. 3 1999.\n \n\n\n\n
\n\n\n\n \n \n $\"Application$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Application of belief networks to water management studies},\n type = {article},\n year = {1999},\n keywords = {Bayesian statistics,Belief networks,Water management},\n pages = {51-57},\n volume = {40},\n websites = {http://www.sciencedirect.com/science/article/pii/S0378377498001036},\n month = {3},\n id = {52597957-5c8b-3805-8775-419b566fe801},\n created = {2015-04-16T00:39:20.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Considerable effort has gone into studying the technical, social, economic and institutional constraints on improving water management in irrigated and rainfed farming systems. Although advances have been made, it can be argued that better progress could have been achieved if more water management studies had involved interdisciplinary data analysis. Such an integrated approach has been hampered primarily by the lack of a mathematical framework that facilitates interdisciplinary data capture and analysis. Belief and decision networks can provide this framework, allowing a simple, integrated methodology for the modelling of complex systems. This paper provides examples of the application of belief and decision networks to specific water management studies in Zimbabwe and Mauritius.},\n bibtype = {article},\n author = {Batchelor, Charles and Cain, Jeremy},\n doi = {10.1016/S0378-3774(98)00103-6},\n journal = {Agricultural Water Management},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n Approximate inference for medical diagnosis.\n \n \n \n \n\n\n \n Wiegerinck, W.; Kappen, H.; ter Braak, E.; ter Burg, W.; Nijman, M.; O, Y.; and Neijt, J.\n\n\n \n\n\n\n Pattern Recognition Letters, 20(11-13): 1231-1239. 11 1999.\n \n\n\n\n
\n\n\n\n \n \n $\"Approximate$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Approximate inference for medical diagnosis},\n type = {article},\n year = {1999},\n keywords = {Bayesian belief networks,Medical decision support,Variational approximations},\n pages = {1231-1239},\n volume = {20},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167865599000902},\n month = {11},\n id = {a88f03b2-ef15-3aa5-be16-7f33420d465b},\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 = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Computer-based diagnostic decision support systems (DSSs) will play an increasingly important role in health care. Due to the inherent probabilistic nature of medical diagnosis, a DSS should preferably be based on a probabilistic model. In particular, Bayesian networks provide a powerful and conceptually transparent formalism for probabilistic modeling. A drawback is that Bayesian networks become intractable for exact computation if a large medical domain is to be modeled in detail. This has obstructed the development of a useful system for internal medicine. Advances in approximation techniques, e.g. using variational methods with tractable structures, have opened new possibilities to deal with the computational problem. However, the only way to assess the usefulness of these methods for a DSS in practice is by actually building such a system and evaluating it by users. In the coming years, we aim to build a DSS for anaemia based on a detailed probabilistic model, and equipped with approximate methods to study the practical feasibility and the usefulness of this approach in medical practice. In this paper, we will sketch how variational techniques with tractable structures can be used in a typical model for medical diagnosis. We provide numerical results on artificial problems. In addition, we describe our approach to develop the Bayesian network for the DSS and show some preliminary results.},\n bibtype = {article},\n author = {Wiegerinck, W.A.J.J. and Kappen, H.J. and ter Braak, E.W.M.T. and ter Burg, W.J.P.P. and Nijman, M.J. and O, Y.L. and Neijt, J.P.},\n doi = {10.1016/S0167-8655(99)00090-2},\n journal = {Pattern Recognition Letters},\n number = {11-13}\n}

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\n \n 1997\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Construction of a Bayesian network for mammographic diagnosis of breast cancer.\n \n \n \n \n\n\n \n Kahn, C., E.; Roberts, L., M.; Shaffer, K., A.; and Haddawy, P.\n\n\n \n\n\n\n Computers in Biology and Medicine, 27(1): 19-29. 1 1997.\n \n\n\n\n
\n\n\n\n \n \n $\"Construction$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Construction of a Bayesian network for mammographic diagnosis of breast cancer},\n type = {article},\n year = {1997},\n keywords = {Artificial intelligence,Bayesian networks,Breast cancer,Computer-aided diagnosis,Expert systems,Mammography},\n pages = {19-29},\n volume = {27},\n websites = {http://www.sciencedirect.com/science/article/pii/S001048259600039X},\n month = {1},\n id = {f127f0e5-e48a-35db-ba65-c3d122342183},\n created = {2015-04-12T18:59:39.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2017-03-14T14:39:12.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Bayesian networks use the techniques of probability theory to reason under uncertainty, and have become an important formalism for medical decision support systems. We describe the development and validation of a Bayesian network (MammoNet) to assist in mammographic diagnosis of breast cancer. MammoNet integrates five patient-history features, two physical findings, and 15 mammographic features extracted by experienced radiologists to determine the probability of malignancy. We outline the methods and issues in the system's design, implementation, and evaluation. Bayesian networks provide a potentially useful tool for mammographic decision support.},\n bibtype = {article},\n author = {Kahn, Charles E. and Roberts, Linda M. and Shaffer, Katherine A. and Haddawy, Peter},\n doi = {10.1016/S0010-4825(96)00039-X},\n journal = {Computers in Biology and Medicine},\n number = {1}\n}

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\n \n undefined\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n (99+) (PDF) Investigating the Use of Bayesian Networks to Provide Decision Support to Military Intelligence Analysts | Venkat Sastry - Academia.edu.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
\n\n\n\n \n \n $\"(99+)$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@misc{\n title = {(99+) (PDF) Investigating the Use of Bayesian Networks to Provide Decision Support to Military Intelligence Analysts | Venkat Sastry - Academia.edu},\n type = {misc},\n websites = {https://www.academia.edu/30437385/Investigating_the_Use_of_Bayesian_Networks_to_Provide_Decision_Support_to_Military_Intelligence_Analysts},\n id = {3b40f410-78ca-3780-adc8-680b852bef70},\n created = {2020-03-05T01:17:42.682Z},\n accessed = {2020-03-04},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {f01630ac-c370-3a2f-839a-0fb9810a9caa},\n last_modified = {2020-03-05T01:17:42.823Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {misc},\n author = {}\n}

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