title: Prognostic Bayesian networks I: rationale, learning procedure, and clinical use.
abstract: Prognostic models are tools to predict the future outcome of disease and disease treatment, one of the fundamental tasks in clinical medicine. This article presents the prognostic Bayesian network (PBN) as a new type of prognostic model that builds on the Bayesian network methodology, and implements a dynamic, process-oriented view on prognosis. A PBN describes the mutual relationships between variables that come into play during subsequent stages of a care process and a clinical outcome. A dedicated procedure for inducing these networks from clinical data is presented. In this procedure, the network is composed of a collection of local supervised learning models that are recursively learned from the data. The procedure optimizes performance of the network's primary task, outcome prediction, and handles the fact that patients may drop out of the process in earlier stages. Furthermore, the article describes how PBNs can be applied to solve a number of information problems that are related to medical prognosis.
authors: Department of Medical Informatics, Academic Medical Center (AMC), P.O. box 22700, 1100 DE Amsterdam, The Netherlands. m.verduijn@amc.uva.nl
authors: Marion
authors: M
authors: Verduijn
authors: Niels
authors: N
authors: Peek
authors: Peter M J
authors: PM
authors: Rosseel
authors: Evert
authors: E
authors: de Jonge
authors: Bas A J M
authors: BA
authors: de Mol
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis