在OSHA投诉中检测PPE问题,使用机器学习来支持传染病爆发响应
1National Personal Protective Technology Laboratory, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Pittsburgh, Pennsylvania.
Journal of occupational and environmental hygiene
|November 19, 2025
概括
机器学习可以分析工作场所安全投诉,以确定个人防护设备 (PPE) 问题. 这种方法提供了有关疫情期间个人防护设备的访问,使用和执法挑战的宝贵数据.
科学领域:
- 职业健康 职业健康 职业健康
- 公共卫生监督 公共卫生监督
- 机器学习应用 机器学习应用
背景情况:
- 在传染病爆发期间,工人面临个人防护设备 (PPE) 的挑战.
- 现有的公共卫生系统缺乏有关员工个人防护设备的实时数据.
- 有效的个人防护设备干预需要及时描述这些问题.
研究的目的:
- 开发和评估一种机器学习 (ML) 方法,用于检测工作场所安全投诉中的个人防护设备问题.
- 评估将职业安全和健康管理局 (OSHA) 投诉用于公共卫生监督的可行性.
- 在传染病爆发期间及时生成有关工人个人防护设备问题的数据.
主要方法:
- 利用了来自COVID-19流行病的78,770个OSHA投诉的数据集.
- 开发和训练了一种机器学习模型,以在投诉文本中检测特定的PPE问题.
- 使用精度和回忆指标评估模型性能.
主要成果:
- 欧沙的投诉揭示了个人防护设备的多样性和数量.
- 该ML模型实现了超过90%的准确性和召回,用于检测无法使用的个人防护设备,缺乏个人防护设备的使用以及不充分的执法.
- 用ML进行的分析确定了工人个人防护设备方面的国家和特定行业的趋势.
结论:
- 机器学习可以有效地重新利用OSHA的投诉,生成有关工人个人防护设备问题的实时数据.
- 这种方法为未来疫情期间的公共卫生监测提供了有价值的工具.
- 需要进一步开发以扩大可检测的个人防护设备问题范围.
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