The peer-reviewed retrospective data study, Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention, published in JACC: Cardiovascular Interventions, evaluated the ability of machine learning models to assess risk for patients who underwent percutaneous coronary intervention (PCI) inside the hospital and following their discharge. The analyzed algorithm was developed by Medial EarlySign data scientists to identify patients at highest risk of complications and hospital readmission after undergoing PCI, one of the most frequently performed procedures in U.S. hospitals. The analysis was based on electronic health records (EHR), demographics, and social data collected from a cohort of 11,709 unique Mayo Clinic patients who underwent 14,349 PCIs during 14,024 hospital admissions. The patients' mean age was 66.9, most were male (71.5%), 45.9% were obese, and 59.8% had a history of heart attacks. The study highlights the potential of AI solutions in supporting cardiology care teams in identifying and treating these high-risk patients.
Sep-6-2019, 23:47:18 GMT