应用机器学习分析工人赔偿数据，以确定特定行业的人体工程学和安全预防重点：俄亥俄州，2001 年至 2011 年。
Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.
机构信息
National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations, and Field Studies, Center for Workers' Compensation Studies, Cincinnati, Ohio (Drs Meyers, Wurzelbacher, Ms Tseng); Ohio Bureau of Workers' Compensation, Division of Safety and Hygiene, Pickerington, Ohio (Dr Al-Tarawneh, Mr Lampl, Mr Robins); National Institute for Occupational Safety and Health, Office of the Director, Economic Research Support Office, Cincinnati, Ohio (Dr Bushnell); National Institute for Occupational Safety and Health, Division of Safety Research, Morgantown, West Virginia (Dr Bell); National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations, and Field Studies, Cincinnati, Ohio (Dr Bertke, Ms Raudabaugh, Dr Schnorr); Taiwan Centers for Disease Control, Taipei City, Taiwan (Dr Wei).
出版信息
J Occup Environ Med. 2018 Jan;60(1):55-73. doi: 10.1097/JOM.0000000000001162.
OBJECTIVE
This study leveraged a state workers' compensation claims database and machine learning techniques to target prevention efforts by injury causation and industry.
METHODS
Injury causation auto-coding methods were developed to code more than 1.2 million Ohio Bureau of Workers' Compensation claims for this study. Industry groups were ranked for soft-tissue musculoskeletal claims that may have been preventable with biomechanical ergonomic (ERGO) or slip/trip/fall (STF) interventions.
RESULTS
On the basis of the average of claim count and rate ranks for more than 200 industry groups, Skilled Nursing Facilities (ERGO) and General Freight Trucking (STF) were the highest risk for lost-time claims (>7 days).
CONCLUSION
This study created a third, major causation-specific U.S. occupational injury surveillance system. These findings are being used to focus prevention resources on specific occupational injury types in specific industry groups, especially in Ohio. Other state bureaus or insurers may use similar methods.
目的
本研究利用州工人赔偿索赔数据库和机器学习技术，针对伤害原因和行业的预防工作。
方法
为这项研究开发了伤害原因自动编码方法，对俄亥俄州工人赔偿局的 120 多万项索赔进行编码。对可能通过生物力学工程学（ERGO）或滑倒/绊倒/跌倒（STF）干预措施预防的软组织肌肉骨骼索赔对行业进行了排名。
结果
基于超过 200 个行业群体的索赔数量和费率排名的平均值，熟练护理设施（ERGO）和普通货运卡车运输（STF）是失时索赔（>7 天）的最高风险。
结论
本研究创建了第三个主要的特定于伤害原因的美国职业伤害监测系统。这些发现正在被用于将预防资源集中在特定行业群体中的特定职业伤害类型上，特别是在俄亥俄州。其他州局或保险公司可能会使用类似的方法。
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