Prioritization of Multi-level Risk Factors, and Predicting Changes in Depression Ratings after Treatment Using Multi-Task Learning

Lu Wang, Mark H. Chignell, Haoyan Jiang, Sachinthya Lokuge, Geneva Mason, Kathryn Fotinos, Martin Katzman

Abstract: Major depressive disorder (MDD) is the most common mental health disorder and is one of the leading preventable causes of death in the United States (U.S.). It is also recognized as a global problem by the World Health organization (WHO). The persistence of MDD leads to many negative consequences including suicide and disability. The Hamilton Rating Scale for Depression (HAM-D) evaluates depression severity based on 17 risk factors (symptoms) of depression. Risk factor analysis is a process to identify and understand the risk factors contributing to a particular disease, and is an essential component in the development of efficient and effective prevention and intervention efforts. Most existing methods use a one-size-fits-all model to identify the risk factors at the population-level. However, this type of method fails to account for data heterogeneity within a population. To overcome this limitation, we formulate a subpopulation specific MDD risk factors (symptoms) ranking problem, under the framework of multi-task learning (MTL), to identify a ranked list of MDD risk factors for each subpopulation (task) simultaneously while utilizing appropriate shared information across tasks. By synchronously learning multiple related tasks, MTL provides a paradigm to rank risk factors both at the subpopulation and population-level. To the best of our knowledge, this is the first study to investigate HAM-D using MTL.