Using Deep Learning to Understand Lack of Exercise
Exercise is generally considered a healthy activity. For example, walking can reduce risk of a cardiovascular event, such as a heart attack. Unfortunately, studies report that roughly half of those over 65 years or older do not regularly exercise. To address this issue, Aetna has programs to encourage exercising. In support, we have built a deep learning model that predicts the probability that a member is exercising and, more importantly, to provide clues as to why a person is not exercising. A sequential model with an attention mechanism estimates the probability of exercise as well as highlights prior events in a person's medical record that most contributed to the estimate. The findings demonstrate good predictive performance, such as 77.2% positive predictive value among the top scoring 10%. Commonly found drivers of not exercising include chronic obstructive pulmonary disease, asthma medication, and depression. Examination of individual cases often reveals clear narratives that help to tailor the exercise program, such as special dance classes for those with a portable oxygen concentrator. More broadly, the work shows how deep learning finds revealing events in a person's healthcare journey with an eye towards providing more effective care.
Ron Xu is a Senior Data Scientist at Aetna, where he utilizes machine learning and deep learning to optimize the care management programs. Prior to that, he has built dozens of models to improve the marketing campaigns for Staples and designed a NLP system to classify customers' feedback in CVS. He holds a M.S. in economics from Suffolk University and a B.S. in statistics from Qingdao Technological University.