@文章{信息:doi/10.2196/24365，作者=“白冉和肖，乐和郭，宇和朱，李学泉，南曦和王，陈亚琛，勤勤和冯，雷和王，英华和余，湘一和王，春雪和胡，永东和刘，占东和谢海勇，王刚，使用被动数字数据的机器学习模型跟踪和监测重度抑郁症患者的情绪稳定性:前瞻性自然主义多中心研究”，期刊=“JMIR Mhealth Uhealth”，年=“2021”，月=“3”，日=“8”，卷=“9”，数=“3”，页=“e24365”，关键词=“数字表型;重度抑郁症;机器学习;背景:重度抑郁症(MDD)是一种常见的精神疾病，其特征是持续的悲伤和对活动失去兴趣。使用智能手机和可穿戴设备来监测重度抑郁症患者的精神状况已经在几项研究中得到了检验。然而，很少有研究使用被动收集的数据来监测情绪随时间的变化。目的:本研究旨在探讨利用被动收集数据(包括手机使用数据、睡眠数据和步数数据)训练的机器学习模型监测重度抑郁症患者情绪状态和稳定性的可行性。方法:我们构建了950个数据样本，代表三次连续患者健康问卷-9评估的时间跨度。每个数据样本都被标记为稳定或情绪波动，根据患者三次就诊的患者健康问卷-9得分，分为稳定-缓解、稳定-抑郁、情绪波动-剧烈和情绪波动-中等亚组。 A total of 252 features were extracted, and 4 feature selection models were applied; 6 different combinations of types of data were experimented with using 6 different machine learning models. Results: A total of 334 participants with MDD were enrolled in this study. The highest average accuracy of classification between Steady and Mood Swing was 76.67{\%} (SD 8.47{\%}) and that of recall was 90.44{\%} (SD 6.93{\%}), with features from all types of data being used. Among the 6 combinations of types of data we experimented with, the overall best combination was using call logs, sleep data, step count data, and heart rate data. The accuracies of predicting between Steady-remission and Mood Swing-drastic, Steady-remission and Mood Swing-moderate, and Steady-depressed and Mood Swing-drastic were over 80{\%}, and the accuracy of predicting between Steady-depressed and Mood Swing-moderate and the overall Steady to Mood Swing classification accuracy were over 75{\%}. Comparing all 6 aforementioned combinations, we found that the overall prediction accuracies between Steady-remission and Mood Swing (drastic and moderate) are better than those between Steady-depressed and Mood Swing (drastic and moderate). Conclusions: Our proposed method could be used to monitor mood changes in patients with MDD with promising accuracy by using passively collected data, which can be used as a reference by doctors for adjusting treatment plans or for warning patients and their guardians of a relapse. Trial Registration: Chinese Clinical Trial Registry ChiCTR1900021461; http://www.chictr.org.cn/showprojen.aspx?proj=36173 ", issn="2291-5222", doi="10.2196/24365", url="https://mhealth.www.mybigtv.com/2021/3/e24365", url="https://doi.org/10.2196/24365", url="http://www.ncbi.nlm.nih.gov/pubmed/33683207" }