Comparison of methods for auto-coding causation of injury narratives
| Autor: | Steven J. Wurzelbacher, Alysha R. Meyers, David C. Robins, A. Measure, Stephen J. Bertke, Michael P. Lampl |
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| Rok vydání: | 2016 |
| Předmět: |
Engineering
Databases Factual Poison control Human Factors and Ergonomics Workers' compensation Computer security computer.software_genre Logistic regression Article Automation 03 medical and health sciences Bayes' theorem Naive Bayes classifier 0302 clinical medicine 0502 economics and business Injury prevention Accidents Occupational Humans Causation Safety Risk Reliability and Quality 050210 logistics & transportation Narration business.industry 05 social sciences Clinical Coding Public Health Environmental and Occupational Health Bayes Theorem Models Theoretical Occupational Injuries 030210 environmental & occupational health Logistic Models Workers' Compensation Artificial intelligence business computer Natural language processing Coding (social sciences) |
| Zdroj: | Accident Analysis & Prevention. 88:117-123 |
| ISSN: | 0001-4575 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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