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, cilt.52, ss.373-385, 2014 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 52
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.jbi.2014.07.018
  • Dergi Adı: JOURNAL OF BIOMEDICAL INFORMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.373-385
  • Anahtar Kelimeler: Clinical decision support, Bayesian networks, Evidence-based medicine, Evidence synthesis, Meta-analysis, INFERENCE
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

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.