Article ID Journal Published Year Pages File Type
6965339 Accident Analysis & Prevention 2016 7 Pages PDF
Abstract
One important routine task in injury research is to effectively classify injury circumstances into user-defined categories when using narrative text. However, traditional manual processes can be time consuming, and existing batch learning systems can be difficult to utilize by novice users. This study evaluates a “Learn-As-You-Go” machine-learning program. When using this program, the user trains classification models and interactively checks on accuracy until a desired threshold is reached. We examined the narrative text of traumatic brain injuries (TBIs) in the National Electronic Injury Surveillance System (NEISS) and classified TBIs into sport and non-sport categories. Our results suggest that the DUALIST “Learn-As-You-Go” program, which features a user-friendly online interface, is effective in injury narrative classification. In our study, the time frame to classify tens of thousands of narratives was reduced from a few days to minutes after approximately sixty minutes of training.
Related Topics
Physical Sciences and Engineering Chemical Engineering Chemical Health and Safety
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