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Knowledge and Information Systems
Published in: Knowledge and Information Systems 1/2017

22-03-2016 | Regular Paper

Clinical evidence framework for Bayesian networks

Authors: Barbaros Yet, Zane B. Perkins, Nigel R. M. Tai, D. William R. Marsh

Published in: Knowledge and Information Systems | Issue 1/2017

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Abstract

There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care.
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Footnotes
1
A preliminary stage of the evidence framework has been briefly described in [49].
 
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Metadata
Title
Clinical evidence framework for Bayesian networks
Authors
Barbaros Yet
Zane B. Perkins
Nigel R. M. Tai
D. William R. Marsh
Publication date
22-03-2016
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 1/2017
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-016-0932-1

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