一个数据驱动的行业特定职业伤害风险和模式的分析
1Department of Big Data Management and Application, School of Maritime Economics and Management, Dalian Maritime University, Dalian, Liaoning, China.
International journal of injury control and safety promotion
|October 9, 2025
概括
这项研究使用了协会规则挖掘 (ARM) 来分析美国职业安全和健康管理局 (OSHA) 的数据,揭示了特定行业的伤害模式和风险因素,以改善职业安全干预措施.
科学领域:
- 职业安全与健康问题 职业安全与健康问题
- 数据挖掘 数据挖掘
- 伤害流行病学 伤害流行病学
背景情况:
- 尽管取得了进展,但预防严重职业伤害仍然是各种行业面临的重大挑战.
- 有效的安全管理需要了解特定行业的伤害趋势和风险因素.
研究的目的:
- 使用美国职业安全与健康管理局 (OSHA) 数据对严重职业伤害进行数据驱动的调查.
- 通过使用协会规则挖矿 (ARM) 和主题分析,识别特定行业的损伤概况和相互关联的风险模式.
主要方法:
- 利用来自美国OSHA的公开可用的严重职业伤害报告.
- 应用协会规则挖掘 (ARM) 来发现风险因素之间同时发生的模式.
- 纳入专题分析,以将发现置于背景,并确定特定行业的损害概况.
主要成果:
- 确定了不同的受伤概况:在制造业中的指部受伤,在建筑业中的落/烧伤,在运输/批发业中的下肢受伤,在零售业中的落,在采矿业中的烧伤/手部受伤以及在医疗保健中的腰部受伤.
- 发现了任务类型,环境条件和受影响的身体部位之间的复杂共发生模式,影响受伤严重程度.
- 突出了特定部门的损伤集群和相关的风险因素.
结论:
- 这些发现为制定有针对性的,特定部门的安全干预提供了宝贵的见解.
- 强调职业伤害数据分析在为基于证据的预防策略提供信息方面发挥的关键作用.
- 数据驱动的方法对于加强职业安全管理和减少严重伤害至关重要.
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