How Machine Learning Is Helping Us Predict Heart Disease and Diabetes
While debate drags on about legislation, regulations, and other measures to improve the U.S. health care system, a new wave of analytics and technology could help dramatically cut costly and unnecessary hospitalizations while improving outcomes for patients. For example, by preventing hospitalizations in cases of just two widespread chronic illnesses — heart disease and diabetes — the United States could save billions of dollars a year.
Toward this end, my colleagues and I at Boston University’s Center for Information and Systems Engineering have been striving to bring the power of machine-learning algorithms to this critical problem. In an ongoing effort with Boston-area hospitals, including the Boston Medical Center and the Brigham and Women’s Hospital, we found that we could predict hospitalizations due to these two chronic diseases about a year in advance with an accuracy rate of as much as 82%. This will give care providers the chance to intervene much earlier and head off hospitalizations. Our team is also working with the Department of Surgery at the Boston Medical Center and can predict readmissions within 30 days of general surgery; the hope is to guide postoperative care in order to prevent them.
The hospitals provide patients’ anonymized electronic health records (EHRs) that contain all of the information the hospital has about each patient, including demographics, diagnoses, admissions, procedures, vital signs taken at doctor visits, medications prescribed, and lab results. We then unleash our algorithms to predict who might have to be hospitalized. Th Continue reading