Poster: Binge drinking risk factors ranking using multi-task learning

Daniel Gallegos, Elaine Raymond, Lu Wang

Abstract: Mental health informatics is the use of data science tools to improve mental health outcomes. Binge drinking is tied to several negative mental health outcomes including increased severity of anxiety and depression symptoms. Many existing methods of identifying binge drinking risk factors fail to account for data heterogeneity within a population. To address this limitation, we formulate a subpopulation binge drinking behavioral risk factors ranking problem, under the framework of multi-task learning (MTL). This outputs a ranked list of binge drinking behavioral risk factors for each subpopulation (task) concurrently while incorporating shared data across tasks. We then analyzed the ranked lists of risk factors and compared the top ranking factors for each MTL setting. We conclude that MTL can be effectively used to obtain risk factors for binge drinking and suggest future research directions.