title: Combining data and meta-analysis to build Bayesian networks for clinical decision support.
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.
authors.affiliation: School of Electronic Engineering and Computer Science, Queen Mary University of London, UK. Electronic address: b.yet@qmul.ac.uk.
authors.forename: Barbaros
authors.initials: B
authors.lastname: Yet
authors.affiliation: Centre for Trauma Science, Queen Mary University of London, UK.
authors.forename: Zane B
authors.initials: ZB
authors.lastname: Perkins
authors.affiliation: The United States Army Medical Research and Materiel Command, MD, USA.
authors.forename: Todd E
authors.initials: TE
authors.lastname: Rasmussen
authors.affiliation: The Royal London Hospital, London, UK.
authors.forename: Nigel R M
authors.initials: NR
authors.lastname: Tai
authors.affiliation: School of Electronic Engineering and Computer Science, Queen Mary University of London, UK.
authors.forename: D William R
authors.initials: DW
authors.lastname: Marsh
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D019317:Evidence-Based Medicine; D006801:Humans; D015201:Meta-Analysis as Topic; D008962:Models, Theoretical; D057772:Vascular System Injuries
keywords: Bayesian networks; Clinical decision support; Evidence synthesis; Evidence-based medicine; Meta-analysis