Combining data and meta-analysis to build Bayesian networks for clinical decision support

Date

2014-12-01

Author

Yet, Barbaros
Rasmussen, Todd E.
Tai, Nigel R. M.
Marsh, D. William R.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Clinical decision support, Meta-analysis, Evidence synthesis, Evidence-based medicine, Bayesian networks

URI

https://hdl.handle.net/11511/56235

Journal

JOURNAL OF BIOMEDICAL INFORMATICS

DOI

https://doi.org/10.1016/j.jbi.2014.07.018

Collections

Graduate School of Informatics, Article

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Citation Formats

B. Yet, T. E. Rasmussen, N. R. M. Tai, and D. W. R. Marsh, “Combining data and meta-analysis to build Bayesian networks for clinical decision support,” JOURNAL OF BIOMEDICAL INFORMATICS, pp. 373–385, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56235.