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通过集群和SHAP解释,在大型零售工作场所的夜班工人代谢风险分层

InHo Lee^1,2, SangHee Hong³, EunChul Jang¹

¹Department of Occupational and Environmental Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea.

Frontiers in public health

|January 28, 2026

概括

夜班工作带来代谢健康风险,但个人的风险模式各不相同. 可解释的人工智能识别了BMI和工作时间等关键因素,为夜工提供了个性化的健康策略.

科学领域:

职业健康职业健康职业健康
代谢健康代谢健康
医疗保健中的人工智能

背景情况:

夜间工作是代谢性疾病的重要职业风险因素.
夜班工作者之间代谢健康风险的个体差异尚未完全理解.
这项研究调查了使用人工智能的夜班工人代谢健康风险的因素.

研究的目的:

识别和说明夜班工人代谢健康风险的因素.
用机器学习将夜班工人分为不同的健康风险组.
为制定量身定制的工业健康战略提供基础.

主要方法:

收集了夜班工人的健康检查数据 (血糖,胆固醇,血压),年龄和工作时间.
雇员无监督学习 (UMAP,K-means集群) 用于根据健康特征对员工进行分组.
利用随机森林和沙普利添加式解释 (SHAP) 来确定影响健康因素.

主要成果:

在夜班工作者中发现了四个不同的健康风险集群.
诸如身体质量指数 (BMI),甘油三血糖指数,血压和工作时间等因素显著区分了这些群体.
具体的集群显示了不同的风险概况,包括健康,高风险 (年龄较大,经验较长),中等风险 (过渡性) 和高风险 (年轻,经验较短,生活方式不规则).

结论:

夜班工作者的代谢健康风险是异质的,可以使用机器学习有效地分层.
这些发现支持制定个性化的职业健康战略.
定制干预措施,包括重新设计工作时间表和个性化预防计划,可以减轻夜班工人的风险.

关键词:

集群集成是指集群集成.可以解释的机器学习代谢风险代谢风险晚班工作是夜班工作.职业健康职业健康职业健康

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