@Article{info:doi/10.2196/14149，作者=“Kim, Heejung and Lee, SungHee and Lee, SangEun and Hong, Soyun and Kang, HeeJae and Kim, Namhee”，标题=“利用生态瞬间评估、Actiwatch数据和机器学习预测抑郁:对独居老年人的观察研究”，期刊=“JMIR Mhealth Uhealth”，年=“2019”，月=“10”，日=“16”，卷=“7”，数=“10”，页=“e14149”，关键词=“老年人;单人家庭;抑郁症;生态瞬时评价;活动检测仪;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在测量老年人的抑郁情绪时存在局限性。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。 Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models). 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. ", issn="2291-5222", doi="10.2196/14149", url="http://mhealth.www.mybigtv.com/2019/10/e14149/", url="https://doi.org/10.2196/14149", url="http://www.ncbi.nlm.nih.gov/pubmed/31621642" }