Expert system for predicting unstable angina based on Bayesian networks

The use of computer-based clinical decision support (CDS) tools is growing significantly in recent years. These tools help reduce waiting lists, minimise patient risks and, at the same time, optimise the cost health resources. In this paper, we present a CDS application that predicts the probability of having unst able angina based on clinical data. Due to the characteristics of the variables (mostly binary) a Bayesian netwo rk model was chosen to support the system. Bayesian-network model was constructed using a populatio n of 1164 patients, and subsequently was validated with a population of 103 patien ts. The validation results, with a negative predictive value (NPV) of 91%, demonstrate its applicability to help clinicians. The final model was implemented as a web application that is currently been validated by clinician specialis ts. 2013 Elsevier Ltd. All rights reserved.