title: Comparison of methods for auto-coding causation of injury narratives.
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.
authors.affiliation: National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations, and Field Studies, Industrywide Studies Branch, 1090 Tusculum Ave, Cincinnati, OH 45226, United States. Electronic address: inh4@cdc.gov.
authors.forename: S J
authors.initials: SJ
authors.lastname: Bertke
authors.affiliation: National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations, and Field Studies, Industrywide Studies Branch, Center for Workers' Compensation Studies, 1090 Tusculum Ave, Cincinnati, OH 45226, United States.
authors.forename: A R
authors.initials: AR
authors.lastname: Meyers
authors.affiliation: National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations, and Field Studies, Industrywide Studies Branch, Center for Workers' Compensation Studies, 1090 Tusculum Ave, Cincinnati, OH 45226, United States.
authors.forename: S J
authors.initials: SJ
authors.lastname: Wurzelbacher
authors.affiliation: Bureau of Labor Statistics, Occupational Safety and Health Statistics, 2 Massachusetts Avenue, Washington, DC 20212, United States.
authors.forename: A
authors.initials: A
authors.lastname: Measure
authors.affiliation: Ohio Bureau of Workers' Compensation, Division of Safety & Hygiene, 13430 Yarmouth Drive, Pickerington, OH 43147, United States.
authors.forename: M P
authors.initials: MP
authors.lastname: Lampl
authors.affiliation: Ohio Bureau of Workers' Compensation, Division of Safety & Hygiene, 13430 Yarmouth Drive, Pickerington, OH 43147, United States.
authors.forename: D
authors.initials: D
authors.lastname: Robins
mesh_terms: D000062:Accidents, Occupational; D001331:Automation / Q000379:methods*; D001499:Bayes Theorem; D059019:Clinical Coding / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D016015:Logistic Models; D008962:Models, Theoretical; D033262:Narration; D060051:Occupational Injuries / Q000209:etiology*; D014942:Workers' Compensation
keywords: Auto-coding; Injury narratives; Naïve Bayes; Regularized logistic regression; Workers’ compensation