Combining data and meta-analysis to build Bayesian networks for clinical decision support. Yet, B., Perkins, Z., B., Rasmussen, T., E., Tai, N., R., M., & Marsh, D., W., R. Journal of biomedical informatics, 52:373-85, 12, 2014.
Website doi abstract bibtex 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.
@article{
title = {Combining data and meta-analysis to build Bayesian networks for clinical decision support.},
type = {article},
year = {2014},
keywords = {Bayesian networks,Clinical decision support,Evidence synthesis,Evidence-based medicine,Meta-analysis},
pages = {373-85},
volume = {52},
websites = {http://www.sciencedirect.com/science/article/pii/S1532046414001816},
month = {12},
id = {74ba4371-b97e-3f7a-ba19-6b7c62005699},
created = {2015-04-12T20:17:35.000Z},
accessed = {2015-04-11},
file_attached = {false},
profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
group_id = {838ecfe2-7c01-38b2-970d-875a87910530},
last_modified = {2017-03-14T14:27:28.880Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
private_publication = {false},
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.},
bibtype = {article},
author = {Yet, Barbaros and Perkins, Zane B and Rasmussen, Todd E and Tai, Nigel R M and Marsh, D William R},
doi = {10.1016/j.jbi.2014.07.018},
journal = {Journal of biomedical informatics}
}
Downloads: 0
{"_id":"hfrTrvtk8C6cEScRP","authorIDs":[],"author_short":["Yet, B.","Perkins, Z., B.","Rasmussen, T., E.","Tai, N., R., M.","Marsh, D., W., R."],"bibbaseid":"yet-perkins-rasmussen-tai-marsh-combiningdataandmetaanalysistobuildbayesiannetworksforclinicaldecisionsupport-2014","bibdata":{"title":"Combining data and meta-analysis to build Bayesian networks for clinical decision support.","type":"article","year":"2014","keywords":"Bayesian networks,Clinical decision support,Evidence synthesis,Evidence-based medicine,Meta-analysis","pages":"373-85","volume":"52","websites":"http://www.sciencedirect.com/science/article/pii/S1532046414001816","month":"12","id":"74ba4371-b97e-3f7a-ba19-6b7c62005699","created":"2015-04-12T20:17:35.000Z","accessed":"2015-04-11","file_attached":false,"profile_id":"95e10851-cdf3-31de-9f82-1ab629e601b0","group_id":"838ecfe2-7c01-38b2-970d-875a87910530","last_modified":"2017-03-14T14:27:28.880Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"private_publication":false,"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.","bibtype":"article","author":"Yet, Barbaros and Perkins, Zane B and Rasmussen, Todd E and Tai, Nigel R M and Marsh, D William R","doi":"10.1016/j.jbi.2014.07.018","journal":"Journal of biomedical informatics","bibtex":"@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 = {74ba4371-b97e-3f7a-ba19-6b7c62005699},\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 = {838ecfe2-7c01-38b2-970d-875a87910530},\n last_modified = {2017-03-14T14:27:28.880Z},\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}","author_short":["Yet, B.","Perkins, Z., B.","Rasmussen, T., E.","Tai, N., R., M.","Marsh, D., W., R."],"urls":{"Website":"http://www.sciencedirect.com/science/article/pii/S1532046414001816"},"biburl":"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0","bibbaseid":"yet-perkins-rasmussen-tai-marsh-combiningdataandmetaanalysistobuildbayesiannetworksforclinicaldecisionsupport-2014","role":"author","keyword":["Bayesian networks","Clinical decision support","Evidence synthesis","Evidence-based medicine","Meta-analysis"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0","creationDate":"2015-04-11T20:28:32.523Z","downloads":0,"keywords":["bayesian networks","clinical decision support","evidence synthesis","evidence-based medicine","meta-analysis"],"search_terms":["combining","data","meta","analysis","build","bayesian","networks","clinical","decision","support","yet","perkins","rasmussen","tai","marsh"],"title":"Combining data and meta-analysis to build Bayesian networks for clinical decision support.","year":2014,"dataSources":["ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}