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Development and evaluation of a Na´ve Bayesian model for coding causation of workers' compensation claims.

Authors
Bertke-SJ; Meyers-AR; Wurzelbacher-SJ; Bell-J; Lampl-ML; Robins-D
Source
J Saf Res 2012 Dec; 43(5-6):327-332
NIOSHTIC No.
20041772
Abstract
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.
Keywords
Analytical-models; Analytical-processes; Mathematical-models; Injuries; Musculoskeletal-system-disorders; Ergonomics; Repetitive-work; Surveillance-programs; Diseases; Accidents; Recording-systems; Computer-models; Computer-software; Safety-research; Industrial-environment; Accident-analysis; Author Keywords: Data-mining; Text-mining; Bayes; Accident narratives; Text classification
Contact
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
CODEN
JSFRAV
Publication Date
20121201
Document Type
Journal Article
Email Address
inh4@cdc.gov
Fiscal Year
2013
NTIS Accession No.
NTIS Price
Identifying No.
B20121218C
Issue of Publication
5-6
ISSN
0022-4375
NIOSH Division
DSHEFS; DSR
Priority Area
Public Safety; Transportation, Warehousing and Utilities; Wholesale and Retail Trade
Source Name
Journal of Safety Research
State
OH; WV
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