使用机器学习模型探索轮班工人失眠严重程度的预测因素
Hyewon Yeo1, Hyeyeon Jang1, Nambeom Kim2
1Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea.
Frontiers in public health
|March 31, 2025
概括
机器学习确定了轮班工人失眠严重性的41个关键预测因素,包括工作特征和心理健康因素. 这种基于数据的模型有助于理解和潜在地管理这一群体的睡眠障碍.
科学领域:
- 职业健康 职业健康 职业健康
- 睡眠医学 睡眠医学
- 数据科学数据科学数据科学
背景情况:
- 轮班工作破坏昼夜节律,导致明显的失眠特征.
- 以前的研究已经探索了轮班工人失眠严重性的有限预测因素.
- 需要采用数据驱动的方法来识别关键预测因素,并为轮班工作者开发一个强大的失眠预测模型.
研究的目的:
- 用机器学习 (ML) 方法识别轮班工人失眠严重性的潜在预测因素.
- 评估基于ML的预测模型对轮班工人失眠的准确性.
- 探索与非轮班工人相比,轮班工人失眠严重性的独特预测因素.
主要方法:
- 在4,572名轮班工人和2,093名非轮班工人中评估了失眠严重性的预测因素.
- 在ML模型开发中使用了具有最少绝对收缩和选择运算符 (LASSO) 的一般线性模型.
- 根据轮班工作时间表进行了额外的分析,以评估基于轮班工作时间表的相互作用效应.
主要成果:
- 从281个变量中确定了41个关键预测因素,包括人口统计,身体健康,工作特征和心理健康因素.
- 轮班工作者显示,失眠严重程度与工作被动性,专制氛围,容易醒来,压力和药物等因素之间的关联更强.
- 机器学习预测模型表现出良好的整体准确性和特异性,与非轮班工人相比,轮班工人的F1得分和召回更好.
结论:
- 机器学习算法有效地识别了轮班工人失眠严重性的关键预测因素,并纳入了工作场所的条件.
- 这些发现与传统的失眠模型一致,但突出了轮班工作的独特特征.
- 为临床应用和未来的研究,建议开发基于ML的全面预测模型,并确定关键预测因子.
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