Development and evaluation of a Naïve Bayesian model for coding causation of workers' compensation claims

Details

• Personal Author:
  Bell J ; Bertke SJ ; Lampl ML ; Meyers, Alysha R. ; Robins D ; Wurzelbacher, Steven J.
• Description:
  Introduction: Tracking and trending rates of injuries and illnesses classified as musculoskeletal disorders caused by ergonomic risk factors such as overexertion and repetitive motion (MSDs) and slips, trips, or falls (STFs) in different industry sectors is of high interest to many researchers. Unfortunately, identifying the cause of injuries and illnesses in large datasets such as workers' compensation systems often requires reading and coding the free form accident text narrative for potentially millions of records. Method: To alleviate the need for manual coding, this paper describes and evaluates a computer auto-coding algorithm that demonstrated the ability to code millions of claims quickly and accurately by learning from a set of previously manually coded claims. Conclusions: The auto-coding program was able to code claims as a musculoskeletal disorders, STF or other with approximately 90% accuracy. Impact on industry: The program developed and discussed in this paper provides an accurate and efficient method for identifying the causation of workers' compensation claims as a STF or MSD in a large database based on the unstructured text narrative and resulting injury diagnoses. The program coded thousands of claims in minutes. The method described in this paper can be used by researchers and practitioners to relieve the manual burden of reading and identifying the causation of claims as a STF or MSD. Furthermore, the method can be easily generalized to code/classify other unstructured text narratives. [Description provided by NIOSH]
• Subjects:
  Accidents, Occupational Ergonomics Manufacturing And Industrial Facilities Musculoskeletal Diseases Occupational Health Safety Software
• Keywords:
  Accident-analysis Accident Narratives Accidents Analytical-models Analytical-processes Author Keywords: Data-mining Bayes Computer-models Computer-software Diseases Industrial-environment Injuries Mathematical-models Musculoskeletal-system-disorders Recording-systems Repetitive-work Safety-research Surveillance-programs Text-mining Text Classification

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• ISSN:
  0022-4375
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 3 ; OSHA Region 5 ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DSHEFS - Division of Surveillance, Hazard Evaluations, and Field Studies ; DSR - Division of Safety Research
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  327-332
• Volume:
  43
• Issue:
  5
• NIOSHTIC Number:
  nn:20041772
• Citation:
  J Safety Res 2012 Dec; 43(5-6):327-332
• Contact Point Address:
  S.J. Bertke, Industrywide Studies Branch, Division of Surveillance, Hazard Evaluations and Field Studies, National Institute for Occupational Safety and Health, 4676 Columbia Parkway, R-15, Cincinnati, OH 45226, USA
• Email:
  inh4@cdc.gov
• Federal Fiscal Year:
  2013
• NORA Priority Area:
  Public Safety ; Transportation, Warehousing and Utilities ; Wholesale and Retail Trade
• Peer Reviewed:
  True
• Source Full Name:
  Journal of Safety Research
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:d43f019636f5063e124dfd01b8eb47c973c52cfb4f003b1f10ab5a622c6cc3b93b5be4a63dc7743dcaf5e4ee18c7ba3c447b769347481305f254ec8d5149ef18
• Download URL:
  https://stacks.cdc.gov/view/cdc/194242/cdc_194242_DS1.pdf
• File Type:
  [PDF - 336.56 KB ]