Prognostic Bayesian networks: II: An application in the domain of cardiac surgery

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.

[1]  W. Knaus,et al.  The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. , 1991, Chest.

[2]  L. Iezzoni.  Risk Adjustment for Measuring Healthcare Outcomes , 1994 .

[3]  Manuel Laguna,et al.  Tabu Search , 1997 .

[4]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[5]  S. Lemeshow,et al.  European system for cardiac operative risk evaluation (EuroSCORE). , 1999, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[6]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[7]  C. Metz.  Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[8]  S. Lemeshow,et al.  A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. , 1993, JAMA.

[9]  Wai Lam,et al.  LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE , 1994, Comput. Intell..

[10]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[11]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[12]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[13]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[14]  R. Salamon,et al.  Risk factors and outcome in European cardiac surgery: analysis of the EuroSCORE multinational database of 19030 patients. , 1999, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[15]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[16]  Evert de Jonge,et al.  Prognostic Bayesian networks: I: Rationale, learning procedure, and clinical use , 2007, J. Biomed. Informatics.