Machine learning techniques can better predict bleeding risk for patients undergoing percutaneous coronary intervention (PCI) than traditional methods, report Yale researchers. This study is published in JAMA Network Open. The research team analyzed data from the American College of Cardiology's (ACC) National Cardiovascular Data Registry (NCDR) from 2009 to 2015 using machine learning, a branch of artificial intelligence capable of performing tasks by inferring patterns in data. The database includes more than 3 million procedures conducted at hospitals across the United States. The team found that machine learning analytics improved the prediction of bleeding risk after PCI (often used to open up blood vessels narrowed by plaque build-up), which could better inform decisions by patients and doctors.