Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.

ABSTRACT:

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 causati

SUBMITTER: Meyers AR 

PROVIDER: S-EPMC5868484 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature