机器学习方法预测人群中抑郁症发病风险的研究进展
Review on machine learning methods in predicting the risk of depression
龚旻炜 1石佳琪 1吴健1
作者信息
- 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
- 折叠
摘要
在维普、万方、知网、Embase、PubMed和Web of Science数据库中检索2019～2023年间有关机器学习方法预测抑郁症发病风险的文献,系统性地总结这些算法的特点、研究领域、模型效能和当前应用所面临的问题和挑战.研究共纳入92篇文献,结果显示,机器学习预测抑郁症发病风险的模型效果较好,最佳预测模型的AUC值为0.6030～0.9976.未来应当建立多中心、前瞻性的融合多模态的动态预测模型,为抑郁症的临床诊断提供更可靠的依据.
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.
关键词
抑郁症/机器学习/深度学习/自然语言处理/预测模型/综述Key words
depression/machine learning/deep learning/natural language processing/prediction model/review引用本文复制引用
基金项目
国家自然科学基金(62176231)
国家自然科学基金(82202984)
出版年
2024
NETL
NSTL
万方数据