Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques
Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques
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DOI：
10.1080/13607863.2022.2031868
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发表时间：
2022-01-23
影响因子：
3.4
通讯作者：
Fang, Ya
中科院分区：
文献类型：
--
作者：
Lin, Shaowu;Wu, Yafei;Fang, Ya
目的：探讨机器学习在居家老年人抑郁症状预测中应用的可能性。方法：收集2011年、2013年、2015年和2018年中国健康与退休纵向研究收集的中国居家老年人抑郁症状数据(n=2650)。采用潜在类生长模型(LCGM)和混合生长模型(GMM)对不同的轨迹类进行分类。基于识别的轨迹模式，采用10倍交叉验证法和接收器工作特征曲线(AUC)下面积的度量，对3种最大似然分类算法(梯度提升决策树、支持向量机和随机森林)进行评估。结果：确定了4种轨迹：无症状(63.9%)、抑郁症状起病{事件增加症状[新发增加(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，包含10个条目的流行病学研究中心抑郁量表(CESD-10)得分、认知、睡眠时间、自我报告记忆是所有轨迹中最重要的五个预测因素。三种预测模型的平均AUC范围为0.661~0.892。结论：ML技术可以很好地预测7年内抑郁症状的发生和发展轨迹，并提供社会人口学和健康信息。
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.