title: Prognostic Bayesian networks II: an application in the domain of cardiac surgery.
abstract: A prognostic Bayesian network (PBN) is new type of prognostic model that implements a dynamic, process-oriented view on prognosis. In a companion article, the rationale of the PBN is described, and a dedicated learning procedure is presented. This article presents an application here of in the domain of cardiac surgery. A PBN is induced from clinical data of cardiac surgical patients using the proposed learning procedure; hospital mortality is used as outcome variable. The predictive performance of the PBN is evaluated on an independent test set, and results were compared to the performance of a network that was induced using a standard algorithm where candidate networks are selected using the minimal description length principle. The PBN is embedded in the prognostic system ProCarSur; a prototype of this system is presented. This application shows PBNs as a useful prognostic tool in medical processes. In addition, the article shows the added value of the PBN learning procedure.
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: Peter M J
authors: PM
authors: Rosseel
authors: Niels
authors: N
authors: Peek
authors: Evert
authors: E
authors: de Jonge
authors: Bas A J M
authors: BA
authors: de Mol
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D013504:Cardiovascular Surgical Procedures / Q000401:mortality*; D006801:Humans; D009426:Netherlands / Q000453:epidemiology; D016571:Neural Networks, Computer; D011183:Postoperative Complications / Q000401:mortality*; D011379:Prognosis; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D016896:Treatment Outcome