Clinical evidence framework for Bayesian networks

Yet, Barbaros
Tai, Nigel R. M.
Marsh, D. William R.
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.


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...
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. (2014-12-01)
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...
Forward problem solution for electrical conductivity imaging via contactless measurements
Gençer, Nevzat Güneri (IOP Publishing, 1999-04-01)
The forward problem of anew medical imaging system is analysed in this study. This system uses magnetic excitation to induce currents inside a conductive body and measures the magnetic fields of the induced currents. The forward problem, that is determining induced currents in the conductive body and their magnetic fields, is formulated. For a general solution of the forward problem, the finite element method (FEM) is employed to evaluate the scalar potential distribution. Thus, inhomogeneity and anisotropy...
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,...
Advanced mathematical and statistical tools in the dynamic modelling and simulation of gene-environment networks
Purutçuoğlu Gazi, Vilda (Springer, 2014-01-01)
In this study, some methodologies and a review of the recently obtained new results are presented for the problem of modeling, anticipation and forecasting of genetic regulatory systems, as complex systems. In this respect, such kind of complex systems are modeled in the dynamical sense into the two different ways, namely, by a system of ordinary differential equations (ODEs) and Gaussian graphical methods (GGM). An artificial time-course microarray dataset of a gene-network is modeled as an example by usin...
Citation Formats
B. Yet, N. R. M. Tai, and D. W. R. Marsh, “Clinical evidence framework for Bayesian networks,” KNOWLEDGE AND INFORMATION SYSTEMS, pp. 117–143, 2017, Accessed: 00, 2020. [Online]. Available: