title: Explicit evidence for prognostic Bayesian network models.
abstract: 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 illustrate this framework by a BN developed with clinical evidence to predict coagulation disorders in trauma care.
authors.affiliation: School of EECS, Queen Mary University of London, UK.
authors.forename: Barbaros
authors.initials: B
authors.lastname: Yet
authors.affiliation: Centre for Trauma Science, Queen Mary University of London, UK.
authors.forename: Zane
authors.initials: Z
authors.lastname: Perkins
authors.affiliation: The Royal London Hospital, London, UK.
authors.forename: Nigel
authors.initials: N
authors.lastname: Tai
authors.affiliation: School of EECS, Queen Mary University of London, UK.
authors.forename: William
authors.initials: W
authors.lastname: Marsh
mesh_terms: D001499:Bayes Theorem; D001778:Blood Coagulation Disorders / Q000175:diagnosis*; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D019317:Evidence-Based Medicine; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity