Deep depression prediction on longitudinal data via joint anomaly ranking and classification

• Main Authors: PANG, Guansong, PHAM, Ngoc Thien Anh, BAKER, Emma, BENTLEY, Rebecca, VAN DEN HENGEL, Anton
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Anomaly detection; Deep learning; Depression prediction; One-class classification; Databases and Information Systems; Longitudinal Data Analysis and Time Series
• Online Access: https://ink.library.smu.edu.sg/sis_research/7544; https://ink.library.smu.edu.sg/context/sis_research/article/8547/viewcontent/Deep_depression_prediction_on_longitudinal_data_via_joint_anomaly_ranking_and_classification.pdf
• Institution: Singapore Management University

A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression currently. In this work we explore the possibility of predicting future depression using machine learning applied to longitudi...