Development And Validation Of A Machine Learning-based Readmission Risk Prediction Model For Non-st Elevation Myocardial Infarction Patients After Percutaneous Coronary Intervention

This article discusses the use of machine learning (ML) methods in medical research, specifically in predicting the readmission of patients with non-ST segment elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI). The study found that ML methods, such as random forests, decision trees, and support-vector machines, showed better predictive value than traditional regression risk scoring systems. The use of ML techniques can help accurately assess the risk of readmission and long-term prognosis for NSTEMI patients.