Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.
Department of Psychiatry, Korea University College of Medicine, Seoul, South Korea.
Sci Data. 2024 Nov 21;11(1):1264. doi: 10.1038/s41597-024-04147-6.
This study investigated the utilization of digital phenotypes and machine learning algorithms to predict impending panic symptoms in patients with mood and anxiety disorders. A cohort of 43 patients was monitored over a two-year period, with data collected from smartphone applications and wearable devices. This research aimed to differentiate between the day before panic (DBP) and stable days without symptoms. With RandomForest, GradientBoost, and XGBoost classifiers, the study analyzed 3,969 data points, including 254 DBP events. The XGBoost model demonstrated performance with a ROC-AUC score of 0.905, while a simplified model using only the top 10 variables maintained an ROC-AUC of 0.903. Key predictors of panic events included evaluated Childhood Trauma Questionnaire scores, increased step counts, and higher anxiety levels. These findings indicate the potential of machine learning algorithms leveraging digital phenotypes to predict panic symptoms, thereby supporting the development of proactive and personalized digital therapies and providing insights into real-life indicators that may exacerbate panic symptoms in this population.
这项研究调查了数字表型和机器学习算法在预测情绪和焦虑障碍患者即将出现的恐慌症状中的应用。一个由 43 名患者组成的队列在两年的时间内接受了监测，数据来自智能手机应用程序和可穿戴设备。这项研究旨在区分恐慌前一天（DBP）和无症状的稳定日。使用 RandomForest、GradientBoost 和 XGBoost 分类器，研究分析了 3969 个数据点，包括 254 个 DBP 事件。XGBoost 模型的表现为 ROC-AUC 得分为 0.905，而仅使用前 10 个变量的简化模型保持了 0.903 的 ROC-AUC。恐慌事件的关键预测因素包括评估的儿童期创伤问卷评分、步幅增加和更高的焦虑水平。这些发现表明，机器学习算法利用数字表型预测恐慌症状具有潜力，从而支持开发主动和个性化的数字疗法，并提供可能加剧该人群恐慌症状的现实生活指标的见解。
