TY -的盟金Heejung AU -李,SungHee AU -李,SangEun盟——香港,Soyun AU -康HeeJae AU -金,Namhee PY - 2019 DA - 2019/10/16 TI -抑郁预测通过生态的评估,图上的数据,和机器学习:观察研究老年人独居乔- JMIR Mhealth Uhealth SP - e14149六世- 7 - 10 KW -老年KW -单人家庭KW -抑郁KW -生态的评估KW -活动检测仪KW -机器学习AB -背景:尽管老年抑郁症很普遍，但在社区环境中使用自我报告工具进行诊断时，测量老年人的抑郁情绪存在局限性。使用可穿戴设备的生态瞬时评估(EMA)可以用于收集数据，将老年人划分为抑郁症组。目的:本研究的目的是开发一种机器学习算法来预测独居老年人抑郁群体的分类。我们专注于利用通过调查、Actiwatch和与抑郁症相关的EMA报告收集的各种数据。方法:采用机器学习技术建立预测模型，分为4个步骤:(1)数据收集，(2)数据处理和表示，(3)数据建模(特征工程和选择)，(4)训练和验证，对预测模型进行测试。独居社区的老年人(N=47)在2017年5月至2018年1月的两周内，每天4次完成EMA报告抑郁情绪。参与者戴着一个活动手表，每30秒测量一次他们的活动和环境光暴露，持续两周。在基线和2周观察结束时，使用韩国版老年抑郁短量表(SGDS-K)和汉密尔顿抑郁评定量表(K-HDRS)评估抑郁症状。建立了基于二元逻辑回归的传统分类模型，并与4种机器学习模型(logit模型、决策树模型、提升树模型和随机森林模型)进行了比较。 Results: On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960). Conclusions: This study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor. SN - 2291-5222 UR - http://mhealth.www.mybigtv.com/2019/10/e14149/ UR - https://doi.org/10.2196/14149 UR - http://www.ncbi.nlm.nih.gov/pubmed/31621642 DO - 10.2196/14149 ID - info:doi/10.2196/14149 ER -