Review on machine learning methods in predicting the risk of depression

VernacularTitle:机器学习方法预测人群中抑郁症发病风险的研究进展
Author: Minwei GONG ¹ ; Jiaqi SHI ; Jian WU
Author Information

1. 浙江大学公共卫生学院,浙江杭州 310058;浙江大学经血管植入器械全国重点实验室,浙江杭州 310000;浙江大学医学院附属第二医院眼科中心,浙江杭州 310000
Keywords: depression; machine learning; deep learning; natural language processing; prediction model; review
From: Chinese Journal of Medical Physics 2024;41(6):776-781
CountryChina
Language:Chinese
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.