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, vol.52, pp.373-385, 2014 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 52
  • Publication Date: 2014
  • Doi Number: 10.1016/j.jbi.2014.07.018
  • Journal Name: JOURNAL OF BIOMEDICAL INFORMATICS
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.373-385
  • Keywords: Clinical decision support, Bayesian networks, Evidence-based medicine, Evidence synthesis, Meta-analysis, INFERENCE

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. (C) 2014 Elsevier Inc. All rights reserved.