Yonghwa Choi  Aram Kim  Minji Jeon  Sunkyu Kim  Kyu-Man Han  Eunsoo Won  Byung-Joo Ham  Jaewoo Kang

10.5626/JOK.2019.46.1.71

depression diagnostic model drug response Machine Learning

Depression is one of the most common mental illnesses in the modern society, and it increases the social burden due to repeated recurrences. However, since there are many pre-disposing factors that cause depression, there is need to develop a machine-learning model that examine these factors effectively. In this paper, we propose a model that can diagnose depression and predict the degree of antidepressant response using four multi modal data including basic information, MRI, genetics, and cognitive test. The model achieved 0.923 AUROC score for diagnosis and 0.08 MSE for prediction of antidepressant response. In addition, the results of the proposed model were quantitatively analyzed, and it confirmed that accurate diagnosis and drug response prediction are possible when the patient’s data is added. Qualitative analysis was also conducted to provide new hypotheses as well as findings on the main factors causing depression.