Comparison of methods for auto-coding causation of injury narratives.

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.

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

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.

Electromyographic and discomfort analysis of confined-space shipyard welding processes.

This study examined muscle fatigue and discomfort in a confined-space welding operation at a shipyard. Surface electromyography (SEMG) was recorded from seven upper extremity and torso muscles of welders welding in a mock-up of the work environment. Following spectral transform of the SEMG data the percentage of the total signal power in the 10-30 Hz frequency band was compared over time during welding. For the conventional stick electrode welding process (SMAW) several muscles exhibited an increase in the percent of the total signal power in the low-frequency band. Fewer muscles exhibited this fatigue-related spectral density shift with a wire welding process (FCAW) the shipyard has considered adopting. This finding suggests that localized muscle fatigue may be reduced by a change to the wire welding process. Subjectively reported discomfort was generally low for both processes, but confirmed the finding that discomfort in the low back and shoulder regions is experienced in this welding operation.