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睡眠,体力活动和恐慌发作:一个为期两年的前性队列研究,使用智能手表,深度学习和可解释的人工智能模型

Chan-Hen Tsai¹, Mesakh Christian², Ying-Ying Kuo³

¹Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei City, Taiwan; Department of Psychiatry, En Chu Kong Hospital, New Taipei City, Taiwan.

|December 28, 2023

概括

深度学习模型可以准确地预测恐慌发作和焦虑症状. 充足的睡眠和体力活动可以降低患有恐慌障碍的人的恐慌发作发生率.

科学领域:

精神病学和行为科学精神病学和行为科学
计算神经科学是一种神经科学.
数字健康数字健康

背景情况:

恐慌失调 (PD) 管理缺乏足够的关于睡眠和体力活动预测作用的数据.
这项研究解决了对恐慌发作 (PA) 和焦虑症状的预测模型的需求.

研究的目的:

使用深度学习预测恐慌发作 (PA),状态焦虑 (SA),特征焦虑 (TA) 和恐慌障碍严重程度 (PDS).
确定影响PD患者PA发生的生活方式因素.

主要方法:

这是一项为期两年的前性队列研究,涉及114名PD患者.
利用深度学习 (RNN,LSTM,GRU) 和SHAP可解释的AI在临床问卷和可穿戴设备的数据上.
收集了关于睡眠,体力活动,心率和焦虑/抑郁等级的数据.

主要成果:

LSTM模型实现了7天预测准确度,PA为92.8%,SA为83.6%,TA为87.2%,PDS为75.6%.
较高的基线焦虑/抑郁分数和并发症预测增加了PA风险.
特定的睡眠持续时间 (6h 23min 10h 50min),深度睡眠 (>50min) 和体力活动 (例如爬楼梯) 与降低PA相关.

结论:

深度学习模型在预测反复的PA和焦虑症状方面表现出高准确度.
优化睡眠和体力活动水平可能是减少PD中PA频率的可行策略.

关键词:

深度学习是一种深度学习.可解释的人工智能心率变化心率的变化.恐慌发作是一种恐慌发作.恐慌失调是一种恐慌失调.智能手表是一个智能手表.

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