Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.

Objectives: This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart failure (CHF) rehospitalization after percutaneous coronary intervention (PCI).

Background: Contemporary risk models for event prediction after PCI have limited predictive ability. Machine learning has the potential to identify complex nonlinear patterns within datasets, improving the predictive power of models.