The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
Aging Ment Health. 2023 Jan;27(1):8-17. doi: 10.1080/13607863.2022.2031868. Epub 2022 Feb 4.
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.
Depressive symptom data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese ( = 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).
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.
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.
我们旨在探索机器学习（ML）在预测居家老年人群中抑郁症状发生和轨迹的可能性，随访时间为 7 年。
本研究纳入了中国健康与养老追踪调查（CHARLS）中居家老年人群的抑郁症状数据（2011 年、2013 年、2015 年和 2018 年采集），共 2650 人。采用潜类别增长模型（LCGM）和增长混合模型（GMM）对不同轨迹类别进行分类。基于所识别的轨迹模式，使用 10 折交叉验证程序和接收者操作特征曲线下面积（AUC）度量值，对 3 种 ML 分类算法（即梯度提升决策树、支持向量机和随机森林）进行评估。
共识别出 4 种抑郁症状轨迹：无症状（63.9%）、抑郁症状发作{新发病例增加症状[新发病例增加（16.8%）]、慢性症状[缓慢减少（12.5%）、持续高（6.8%）]}。在分析的基线变量中，10 项流行病学研究中心抑郁量表（CESD-10）评分、认知、睡眠时间、自我报告的记忆力是所有轨迹中最重要的前 5 个预测指标。3 种预测模型的平均 AUC 范围为 0.661 至 0.892。
ML 技术可以使用易于获取的社会人口学和健康信息，稳健地预测 7 年内抑郁症状的发生和轨迹。
