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{yet2014combining, 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.}, Author = {Yet, Barbaros and Perkins, Zane B and Rasmussen, Todd E and Tai, Nigel RM and Marsh, D William R}, Date-Modified = {2016-11-05 23:34:14 +0000}, Doi = {10.1016/j.jbi.2014.07.018}, Journal = {Journal of biomedical informatics}, Pages = {373--385}, Publisher = {Academic Press}, Title = {Combining data and meta-analysis to build Bayesian networks for clinical decision support}, Volume = {52}, Year = {2014}, Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.jbi.2014.07.018}}

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