Abstract

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Title:: Review on machine learning methods in predicting the risk of depression

Author(s):: GONG Minwei1; 2; 3; SHI Jiaqi1; 2; 3; WU Jian1; 2; 3; 1. School of Public Health, Zhejiang University, Hangzhou 310058, China 2. State Key Laboratory of Transvascular Implantation Devices, Zhejiang University, Hangzhou 310000, China 3. Eye Center, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China

Keywords:: Keywords: depression machine learning deep learning natural language processing prediction model review

Abstract:: Abstract: The articles on machine learning methods for predicting the risk of depression between 2019 and 2023 are retrieved from 6 databases (VIP, WANFANG, CNKI, Embase, PubMed and Web of Science). The review systematically summarized the algorithm characteristics, research fields, model performance, and current problems and challenges. A total of 92 articles are includes. The analysis results show that the machine learning models for predicting the risk of depression perform well, with the AUC values of the best prediction models ranging from 0.603 0 to 0.997 6. In the future, there should be a construction of multicenter prospective dynamic prediction models that use a multi-modal fusion approach to provide a more reliable basis for the clinical diagnosis of depression.