Can AI help predict which heart-failure patients will worsen within a year?

Researchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance. Alex Ouyang | Abdul Latif Jameel Clinic for Machine Learning in Health Publication Date: March 12, 2026 Press Inquiries Press Contact: Alex Ouyang Email: ouyanga@mit.edu Abdul Latif Jameel Clinic for Machine Learning in Health Close Caption: MIT PhD students Tiffany Yau (left) and Teya Bergamaschi are two of the co-first authors behind a new paper introducing a deep learning model that can predict which patients with heart failure are at risk of having their condition worsen up to a year in advance. Credits: Photo: Alex Ouyang/MIT Jameel Clinic Previous image Next image Characterized by weakened or damaged heart musculature, heart failure results in the gradual buildup of fluid in a patient’s lungs, legs, feet, and other parts of the body. The condition is chronic and incurable, often leading to arrhythmias or sudden cardiac arrest. For many centuries, bloodletting and leeches were the treatment of choice, famously practiced by barber surgeons in Europe, during a time when physicians rarely operated on patients. In the 21st century, the management of heart failure has become decidedly less medieval: Today, patients undergo a combination of healthy lifestyle changes, prescription of medications, and sometimes use pacemakers. Yet heart failure remains one of the leading causes of morbidity and mortality,...