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 unstable angina based on clinical data. Due to the characteristics of the variables (mostly binary) a Bayesian network model was chosen to support the system. Bayesian-network model was constructed using a population of 1164 patients, and subsequently was validated with a population of 103 patients. 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 specialists.

• We have developed a Clinical Decision Support System (CDSS) with predicts unstable angina on incoming emergency patients with unspecific chest pain that are on risk of a heart attack.
• The system achieves a 91.67% negative predictive value (NPV) over the validation dataset.
• The CDSS is based on a Bayesian Network with 17 patient-related inputs.
• The system runs as a web application, so that it can be used quickly and efficiently from any computer running a web browser.