TY - JOUR
T1 - Prognostic Bayesian networks I: Rationale, learning procedure, and clinical use
AU - Verduijn, Marion
AU - Peek, Niels
AU - Rosseel, Peter M. J.
AU - de Jonge, Evert
AU - de Mol, Bas A. J. M.
PY - 2007
Y1 - 2007
N2 - 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. (C) 2007 Elsevier Inc. All rights reserved
AB - 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. (C) 2007 Elsevier Inc. All rights reserved
U2 - https://doi.org/10.1016/j.jbi.2007.07.003
DO - https://doi.org/10.1016/j.jbi.2007.07.003
M3 - Article
C2 - 17704008
SN - 1532-0464
VL - 40
SP - 609
EP - 618
JO - Journal of biomedical informatics
JF - Journal of biomedical informatics
IS - 6
ER -