Humana-Mays Healthcare Analytics 2020 Case Competition. Our team members are Yue Li, Feichi Yang and Yifan Shi.
Social determinants of health (SDoH) are becoming more and more important in maintaining the overall health of human-beings. Transportation accessibility, which is fundamental for individuals’ need to engage with their community, is one of them. As such, accurately predicting whether or not someone will be facing transportation challenges opens many opportunities for healthcare companies to better know about its members, not only helpingthose in need but also helping them in advance. Using one-year longitudinal data with 800+ features, we built a deep neural network model after applying natural language processing, which could predict whether a Medicare member will encounter a transportation issue with an AUC of .7406. In addition to prediction, our model offers interpretable insights that allow us to formulate recommendations related to sharing actionable future steps with Humana.