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韩国移民工人致命职业伤害的特征:一项机器学习研究

Ju-Yeun Lee¹, Woojoo Lee¹, Sung-Il Cho^1,2

¹The Department of Public Health, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.

|October 9, 2023

概括

移民工人中致命的职业伤害显示出明显的性别差异,男性面临更高的风险. 针对性别的预防策略和详细的工作分析对于减少移徙工人死亡率至关重要.

科学领域:

职业健康和安全职业健康和安全
流行病学流行病学
医疗保健中的人工智能

背景情况:

对职业伤害的分析对于预防至关重要,但移民工人的严重伤害缺乏性别特定的评估.
了解移民工人死亡职业伤害的性别差异对于有针对性的干预至关重要.

研究的目的:

确定移徙工人的致命职业伤害的性别特征.
用新的分析方法分析这些因素之间的相互作用.
为了比较可解释的人工智能 (XAI) 与传统物流回归 (LR) 的实用性.

主要方法:

对韩国67576名移民工人 (2007-2018年) 的职业伤害国家统计数据的分析.
极端梯度提升模型的应用.
开发和比较夏普利添加式扩展 (SHAP) 和物流回归 (LR) 模型.

主要成果:

观察到显著的性别差异:同一个职业的男性死亡风险高于女性.
职业是女性移民工人死亡的重要预测因素,护理工作显示死亡风险最高.
SHAP在自动交互分析,特征贡献确定和整体性能方面表现出优势,尽管无法确定因果关系.

结论:

针对性别的职业伤害预防干预措施和详细的职业特定分析是必要的,以减少移徙工人的死亡率.
可解释的人工智能 (XAI) 方法,特别是SHAP,为流行病学研究提供了有价值的补充方法,克服了传统统计分析的局限性.

关键词:

性别差异的性别差异机器学习是机器学习.移徙工人是移徙工人的一个群体.职业事故职业事故职业事故职业伤害在职伤害沙普利添加剂的解释 (SHAP)

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