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...
Random effects’ distribution assumption on joint mixed modelling
Özdemir, Celal Oğuz; Kalaylıoğlu Akyıldız, Zeynep Işıl; Department of Statistics (2018)
Joint mixed model is an appealing approach in medical research where it is critical to estimate the odds of a fatal complication that occurs to a patient given the covariate profile such as a risk factor observed over time. For this kind of estimation, joint mixed model is used. In the standard Bayesian analysis of the model, the error variance and random effects’ variance-covariance matrix are apriori modeled independently with Inverse-Gamma and Inverse-Wishart distributions respectively. Recently however,...
Ranking using PROMETHEE when weights and thresholds are imprecise: A data envelopment analysis approach
Karasakal, Esra; Karasakal, Orhan (2021-08-01)
Multicriteria decision making (MCDM) provides tools for the decision makers (DM) to solve complex problems with multiple conflicting criteria. Scalarization of criteria values requires using weights for criteria. Determining weights creates controversy as they are influential on the final ranking and challenges the DM as they are hard to elicit. PROMETHEE method is widely used in MCDM for ranking the alternatives and appropriate in situations when there is limited information on the preference structure of ...
A parallel between regret theory and outranking methods for multicriteria decision making under imprecise information
Ozerol, Gul; Karasakal, Esra (Springer Science and Business Media LLC, 2008-08-01)
Incorporation of the behavioral issues of the decision maker (DM) is among the aspects that each Multicriteria Decision Making (MCDM) method implicitly or explicitly takes into account. As postulated by regret theory, the feelings of regret and rejoice are among the behavioral issues associated with the entire decision making process. Within the context of MCDM, the DM may feel regret, when the chosen alternative is compared with another one having at least one better criterion value. PROMETHEE II is a wide...
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: