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

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


Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences
Constantinou, Anthony Costa; Yet, Barbaros; Fenton, Norman; Neil, Martin; Marsh, William (2016-01-01)
Objectives: Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision.
Towards a Method for Building Causal Bayesian Networks for Prognostic Decision Support
Yet, Barbaros; Marsh, William; Fenton, Norman (null; 2011-07-02)
We describe a method of building a decision support system for clinicians deciding between interventions, using Bayesian Networks (BNs). Using a case study of the amputation of traumatically injured extremities, we explain why existing prognostic models used as decision aids have not been successful in practice. A central idea is the importance of modeling causal relationships, both so that the model conforms to the clinicians ‟ way of reasoning and so that we can predict the probable effect of the availabl...
Shared decision making: a serious game based on OSCE for clinicians
Sülün, Ahmet; Yet, Barbaros; Department of Cognitive Sciences (2022-8-31)
Shared decision making(SDM) is an essential process to improve outcomes and reflect patient views in medical consultation. In SDM, the treatment that patient will take is a decision reached by the participation of both the clinician and the patient. To achieve SDM, clinicians should explain possible treatment options with their pros and cons and encourage patients to join in the process, and patients should state their ideas about the options and preferences about the outcomes. This process follows three st...
Explicit Evidence for Prognostic Bayesian Network Models
Yet, Barbaros; Tai, Nigel; Marsh, William (2014-01-01)
Many prognostic models are not adopted in clinical practice regardless of their reported accuracy. Doubts about the basis of the model is considered to be a major reason for this as the evidence behind clinical models is often not clear to anyone other than their developers. We propose a framework for representing the evidence behind Bayesian networks (BN) developed for prognostic decision support. The aim of this evidence framework is to be able to present all the evidence alongside the BN itself. We illus...
Decision support system for Warfarin therapy management using Bayesian networks
Yet, Barbaros; Raharjo, Hendry; Lifvergren, Svante; Marsh, William; Bergman, Bo (2013-05-01)
Warfarin therapy is known as a complex process because of the variation in the patients' response. Failure to deal with such variation may lead to death as a result of thrombosis or bleeding. The possible sources of variation such as concomitant illnesses and drug interactions have to be investigated by the clinician in order to deal with the variation. This paper describes a decision support system (DSS) using Bayesian networks for assisting clinicians to make better decisions in Warfarin therapy managemen...
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: