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 hereof 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.
