Speaker: Chun-An (Joe) Chou (MIE, Northeastern University)

Abstract: Accurate medical diagnosis and prediction tools are particularly important to help one make better decisions on personalized treatment and intervention. Various predictive models/approaches were developed, and successfully achieved very high accuracy. However, most current tools built using machine learning techniques as a "black box" are not preferable because they fail to provide transparent information, e.g., what-if decision rules. In this talk, we present a combinatorial optimization approach to building accurate and interpretable decision models, driven by data, and demonstrate several practical cases compared to state-of-the-art machine learning methods.