基于可穿戴设备的人工智能在检测抑郁症方面的表现:系统审查和元分析
Jiawen Liu1,2, Junhui Wang3, Zhaobin Wu4
1Liuzhou Railway Vocational Technical College, 2 Wenyuan Road, Yufeng District, Liuzhou, 545000, China, 60 11 1667 0058.
JMIR mental health
|March 10, 2026
概括
使用可穿戴传感器的人工智能 (AI) 模型在检测抑郁症方面显示出高准确度. 虽然对于检测是有效的,但它们在预测抑郁症发作中的实用性是适度的,数据类型等因素会影响结果.
科学领域:
- 数字健康数字健康
- 人工智能的人工智能是人工智能.
- 心理健康技术 心理健康技术
背景情况:
- 可穿戴式传感器技术和人工智能为抑郁症检测和监测提供了新的途径.
- 这些领域的进步使得心理健康评估的新方法成为可能.
研究的目的:
- 用可穿戴设备数据系统地审查和元分析人工智能模型的性能,用于抑郁症检测和预测.
- 探索影响这些AI模型准确性和实用性的因素.
主要方法:
- 在PubMed,Embase,Web of Science和PsycINFO数据库中按照PRISMA-DTA指南进行了搜索.
- 包括16项研究 (32个数据集) 对可穿戴数据的AI算法进行分析,用于抑郁症检测或预测情节.
- 使用双变的随机效应模型组合诊断准确度指标 (灵敏度,特异性,AUC);用PROBAST+AI评估偏差风险.
主要成果:
- 对于抑郁症检测,聚合灵敏度为0.89和特异性为0.93 (AUC=0.96).
- 随机森林模型显示出优异的性能 (灵敏度=0.89,特异性=0.91,AUC=0.97).
- 抑郁情节预测显示,聚合灵敏度为0.86和特异性为0.65;研究设计和人工智能方法影响了准确性.
结论:
- 使用可穿戴设备的AI模型在抑郁症检测方面表现出高准确度,在预测情节方面具有中等效用.
- 异质性,依赖回顾性数据和缺乏标准化可能会限制概括性.
- 需要使用标准化方法和多样化的数据集进行进一步的研究,以提高预测能力.
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