Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques

接收机工作特性 萧条(经济学) 纵向研究 潜在增长模型 抑郁症状 公制(单位) 老人忧郁量表 医学 机器学习 人工智能 心理学 认知 老年学 精神科 计算机科学 经济 病理 宏观经济学 运营管理
作者
Shaowu Lin,Yafei Wu,Lingxiao He,Ya Fang
出处
期刊:Aging & Mental Health [Routledge]
卷期号:27 (1): 8-17 被引量:18
标识
DOI:10.1080/13607863.2022.2031868
摘要

Objectives Our aim was to explore the possibility of using machine learning (ML) in predicting the onset and trajectories of depressive symptom in home-based older adults over a 7-year period.Methods Depressive symptom data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2650) recruited in the China Health and Retirement Longitudinal Study (CHARLS) were included in the current analysis. The latent class growth modeling (LCGM) and growth mixture modeling (GMM) were used to classify different trajectory classes. Based on the identified trajectory patterns, three ML classification algorithms (i.e. gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUC).Results Four trajectories were identified for the depressive symptoms: no symptoms (63.9%), depressive symptoms onset {incident increasing symptoms [new-onset increasing (16.8%)], chronic symptoms [slowly decreasing (12.5%), persistent high (6.8%)]}. Among the analyzed baseline variables, the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) score, cognition, sleep time, self-reported memory were the top five important predictors across all trajectories. The mean AUCs of the three predictive models had a range from 0.661 to 0.892.Conclusions ML techniques can be robust in predicting depressive symptom onset and trajectories over a 7-year period with easily accessible sociodemographic and health information.Supplemental data for this article is available online at http://dx.doi.org/10.1080/13607863.2022.2031868
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