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机器学习模型使用可穿戴设备识别即将发生自杀风险的个人:试点研究

Jumyung Um1, Jongsu Park2, Dong Eun Lee3

  • 1Industrial & Management System Engineering, Kyung Hee University, Yongin, Republic of Korea.

Psychiatry investigation
|February 28, 2025
PubMed
概括
此摘要是机器生成的。

商业可穿戴设备在识别立即自杀风险的个体方面表现有前途. 使用设备数据的机器学习模型,包括心率变化,有效地预测了抑郁症参与者的自杀风险.

关键词:
每天的情绪监测,每天的情绪监测.抑郁症 抑郁症 抑郁症迫在眉的自杀风险即将发生.风险监测 风险监测 风险监测自杀自杀的自杀是自杀的可穿戴设备是一种可穿戴设备.

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科学领域:

  • 数字健康数字健康
  • 精神病学是一个精神病学.
  • 机器学习在医疗保健中的应用

背景情况:

  • 识别立即有自杀风险的个体是心理健康护理中的一个关键挑战.
  • 现有的方法可能无法捕捉与急性自杀念头相关的实时生理和行为变化.

研究的目的:

  • 评估商业可用的可穿戴设备在识别立即自杀风险的个人的有效性.
  • 为了比较单级和多级机器学习模型在使用可穿戴设备数据预测自杀风险方面的性能.

主要方法:

  • 39名患有急性抑郁症的参与者和20名健康对照者在两个月内戴着可穿戴设备.
  • 收集的数据包括活动,睡眠,心率和心率变化;参与者每天自我报告情绪.
  • 机器学习模型是基于汉密尔顿抑郁评分表 (HAMD-3) 评分来预测自杀风险的.

主要成果:

  • 单级和多级模型都准确地预测了即将发生的自杀风险.
  • 与单一级模型 (0.88) 相比,多级模型在曲线下的面积 (0.89) 更高.
  • 多级模型中的关键预测因素包括HAMD总分和心率变化;在单级模型中,HAMD总分和诊断是显著的.

结论:

  • 商业上可用的可穿戴设备是实时识别自杀风险的有希望的工具.
  • 建议通过增强时间分辨率进行进一步的研究,以改进预测能力.