A continuous time Bayesian network model for cardiogenic heart failure

Continuous time Bayesian networks are used to diagnose cardiogenic heart failure and to anticipate its likely evolution. The proposed model overcomes the strong modeling and computational limitations of dynamic Bayesian networks. It consists of both unobservable physiological variables, and clinically and instrumentally observable events which might support diagnosis like myocardial infarction and the future occurrence of shock. Three case studies related to cardiogenic heart failure are presented. The model predicts the occurrence of complicating diseases and the persistence of heart failure according to variations of the evidence gathered from the patient. Predictions are shown to be consistent with current pathophysiological medical understanding of clinical pictures.