Cardiologist Jennifer Ho sees many patients with heart failure at Massachusetts General Hospital. For about half of her patients, it’s not clear how to treat them. They have a subtype of the disease, called heart failure with preserved ejection fraction (HFpEF, pronounced “heff-peff”), that has a complicated mix of risk factors and no approved therapies. To better understand the causes of HFpEF, Ho, also an associated scientist at the Broad Institute of MIT and Harvard, is collaborating with Broad data scientists and cardiologists to use machine learning algorithms to analyze large sets of clinical data from patients with HFpEF. Figuring out how the variety of risk factors such as blood pressure, body mass index, age, and others fit together is challenging with existing tools. By turning to machine learning, Ho, a faculty member with Mass General’s Cardiovascular Research Center and a member of Broad’s Cardiovascular Disease Initiative, and her collaborators hope to uncover previously imperceptible patterns in all that patient data that could help them better understand how this kind of heart failure progresses. The work is part of a larger collaboration between the Broad Institute and Bayer Healthcare that’s aimed at finding new treatments for patients with HFpEF.
