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Development and evaluation of a Naïve Bayesian model for coding causation of workers' compensation claims
S J Bertke1, A R Meyers, S J Wurzelbacher
1National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations, and Field Studies, Industrywide Studies Branch, 4676 Columbia Parkway, Cincinnati, OH 45226, USA. inh4@cdc.gov
Journal of Safety Research
|December 5, 2012
Summary
A new computer algorithm accurately codes millions of workers' compensation claims for musculoskeletal disorders (MSDs) and slips, trips, or falls (STFs) in minutes. This automated method significantly reduces manual data entry burdens for researchers and practitioners.
Area of Science:
- Occupational Health
- Data Science
- Public Health
Background:
- Tracking musculoskeletal disorders (MSDs) and slips, trips, or falls (STFs) is crucial for workplace safety research.
- Manual coding of large workers' compensation datasets is time-consuming and resource-intensive.
Purpose of the Study:
- To develop and evaluate a computer auto-coding algorithm for classifying injury causation in workers' compensation claims.
- To automate the identification of MSDs and STFs from unstructured accident narratives.
Main Methods:
- An algorithm was trained on a dataset of manually coded workers' compensation claims.
- The algorithm was evaluated for its speed and accuracy in coding millions of claims.
Main Results:
- The auto-coding program achieved approximately 90% accuracy in classifying claims as MSD, STF, or other.
- The algorithm processed thousands of claims in minutes, demonstrating high efficiency.
Conclusions:
- The developed program offers an accurate and efficient method for identifying MSD and STF causation in large claim databases.
- This automated approach can be generalized to classify other types of unstructured text narratives in various fields.
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