为轮班工作者提供个性化医生辅助睡眠建议:算法开发和验证研究
Yufei Shen1,2, Alicia Choto Olivier1, Han Yu1
1Rice University, Houston, TX, United States.
JMIR formative research
|April 1, 2025
概括
机器学习算法可以预测轮班工人个性化的睡眠建议,改善轮班工作睡眠障碍的管理. 这项技术显示出对自动化,可信的推的前景.
科学领域:
- 睡眠医学 睡眠医学
- 人工智能的人工智能
- 职业健康 职业健康 职业健康
背景情况:
- 轮班工作破坏昼夜节律,导致轮班工作睡眠障碍.
- 失眠的认知行为治疗 (CBT-I) 原则指导有效的管理策略.
- 个性化干预对于解决个人睡眠障碍至关重要.
研究的目的:
- 开发和评估用于预测医生提供的睡眠建议的机器学习 (ML) 算法.
- 创建一个系统,为轮班工作者提供个性化的睡眠和行为建议.
- 利用可穿戴设备和调查数据进行个性化睡眠干预.
主要方法:
- 收集了日本61名轮班工人的5周的Fitbit,调查和睡眠建议数据.
- 设计了生理和行为特征,然后使用等级分类来识别参与者集群.
- 探索随机森林,光梯度增强机器和CatBoost模型,使用各种数据平衡技术来预测频繁的建议消息.
主要成果:
- 参与者集群因工作轮班和独特的行为模式而有所区别,与睡眠质量相关.
- 机器学习模型显著超过了基线预测 (P<.001),在精度回忆曲线下实现了高面积.
- 功能重要性分析显示,模型准确地使用了相关数据,例如,卧室亮度用于"使卧室变暗"的建议.
结论:
- 一个准确的ML算法显示了自动化,可靠的睡眠建议产生的潜力,轮班工作者.
- 目前的系统虽然有希望,但仅限于预测最频繁的7个建议信息.
- 需要进一步的研究,以扩大预测能力到不太常见的睡眠建议建议.
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