在中国济宁的煤矿工人中,与工作有关的肌肉骨疾病的决定因素:开发一个预测风险模型

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Jiali Li¹, Xuemei Zhang², Yuchen Li¹

¹Department of Labor Hygiene and Environmental Hygiene, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.

Frontiers in public health

|February 23, 2026

概括

与工作相关的肌肉骨疾病 (WMSD) 影响了82%的中国煤矿工人,部,肩膀和腰部疼痛是最常见的. 机器学习模型可以预测WMSD风险,帮助有针对性的预防策略.

科学领域:

职业健康职业健康职业健康
流行病学流行病学
数据科学数据科学数据科学

背景情况:

与工作相关的肌肉骨疾病 (WMSD) 是煤矿工人的重大职业健康问题.
高频率的WMSD威胁到煤矿劳动力的福利.
识别风险因素对于减轻WMSD在这个人群中至关重要.

研究的目的:

确定在中国济南煤矿工人中导致WMSD的关键因素.
开发一个预测模型来评估WMSD在这个职业群体中的风险.
通过数据驱动的洞察力增强职业健康战略.

主要方法:

一项针对1409名煤矿工人的调查,使用修改后的中国肌肉问卷 (CMQ).
后勤回归分析以确定流行率和风险因素.
机器学习算法的应用,包括神经网络和集合方法,用于预测建模.

主要成果:

12个月的WMSD患病率为82%,部,肩膀和腰部受影响最多.
重要风险因素包括吸烟,感知健康状况和不舒服的工作姿势 (p < 0.05).
神经网络模型显示出高预测性能 (AUC 0.886训练,0.704测试),整体模型的表现优于个体模型.

结论:

在中国的煤矿工人中,WMSD非常普遍,受到个人和工作相关因素的影响.
机器学习,特别是组合方法,显示了准确预测WMSD风险的潜力.
这些发现支持为煤矿工人制定有针对性的预防计划.

关键词:

煤矿工人煤矿工人煤矿工人机器学习是机器学习.预测模型是一个预测模型.有关风险因素的风险因素.与工作相关的肌肉骨疾病.

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