Comparison of methods for auto-coding causation of injury narratives

Details

• Alternative Title:
  Accid Anal Prev
• Personal Author:
  Bertke, S.J. ; Meyers, A.R. ; Wurzelbacher, S.J. ; Measure, A. ; Lampl, M.P. ; Robins, D.
• Description:
  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.
• Subjects:
  Accidents, Occupational Article Auto-coding Automation Bayes Theorem Clinical Coding Databases, Factual Humans Injury Narratives Logistic Models Models, Theoretical Narration Naïve Bayes Occupational Injuries Regularized Logistic Regression Workers' Compensation Workers’ Compensation

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• Source:
  Accid Anal Prev. 88:117-123.
• Pubmed ID:
  26745274
• Pubmed Central ID:
  PMC4915551
• Document Type:
  Journal Article
• Funding:
  CC999999/Intramural CDC HHS/United States
• Volume:
  88
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:6cb0067532958c3ca4177639c928d0b90f379711cf4eaa1167ea7dbccfedf43a
• Download URL:
  https://stacks.cdc.gov/view/cdc/40288/cdc_40288_DS1.pdf
• File Type:
  [PDF - 205.14 KB ]