Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions

• Published in: European heart journal Vol. 45; no. 8; pp. 601 - 609
• Main Authors: Hamilton, David E, Albright, Jeremy, Seth, Milan, Painter, Ian, Maynard, Charles, Hira, Ravi S, Sukul, Devraj, Gurm, Hitinder S
• Format: Journal Article
• Language: English
• Published: England 21.02.2024
• Subjects: Acute Kidney Injury - etiology; Hemorrhage - etiology; Humans; Machine Learning; Patient Preference; Percutaneous Coronary Intervention - methods; Renal Dialysis; Risk Assessment - methods; Risk Factors; Stroke - etiology; Treatment Outcome; Risk prediction; Percutaneous coronary intervention; Machine learning

Predicting personalized risk for adverse events following percutaneous coronary intervention (PCI) remains critical in weighing treatment options, employing risk mitigation strategies, and enhancing shared decision-making. This study aimed to employ machine learning models using pre-procedural varia...