Comparison of methods for auto-coding causation of injury narratives. (March 2016)

• Record Type: Journal Article
• Authors: Bertke, S.J.; Meyers, A.R.; Wurzelbacher, S.J.; Measure, A.; Lampl, M.P.; Robins, D.
• Is Part Of: Accident analysis and prevention. Volume 88(2016)
• Page Start: 117
• Page End: 123
• Publication Date: 2016-03
• Subjects: Auto-coding -- Naïve Bayes -- Regularized logistic regression -- Injury narratives -- Workers' compensation; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2015.12.006
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: The auto-coder assigned 2-digit OIICS event/exposure codes with over 70% accuracy. Regularized logistic regression outperformed Naïve Bayes in identifying event/exposure. Sequences of words and single keywords in a single model improved accuracy. The programs and weights used in this paper are available upon request. Abstract: Manually reading free-text narratives in large databases to identify the cause of an injury can be very time consuming and recently, there has been much work in automating this process. In particular, the variations of the naïve Bayes model have been used to successfully auto-code free text narratives describing the event/exposure leading to the injury of a workers' compensation claim. This paper compares the naïve Bayes model with an alternative logistic model and found that this new model outperformed the naïve Bayesian model. Further modest improvements were found through the addition of sequences of keywords in the models as opposed to consideration of only single keywords. The programs and weights used in this paper are available upon request to researchers without a training set wishing to automatically assign event codes to large data-sets of text narratives. The utility of sharing this program was tested on an outside set of injury narratives provided by the Bureau of Labor Statistics with promising results.