Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data

Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative scarcity of instances of depression in the data. In this work we introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking. The auxiliary tasks introduce an inductive bias that improves the classification model's generalizability on small depression samples. Further, unlike existing studies that focus on learning depression signs from static data without considering temporal dynamics, we focus on longitudinal data because i) temporal changes in personal development and family environment can provide critical cues for psychiatric disorders and ii) it may enable us to predict depression before the illness actually occurs. Extensive experimental results on child depression data show that our model is able to i) achieve nearly perfect performance in depression detection and ii) accurately predict depression 2-4 years before the clinical diagnosis, substantially outperforming seven competing methods.