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Computerized “Learn-As-You-Go” Classification of Traumatic Brain Injuries Using NEISS Narrative Data
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Feb 3 2016
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Source: Accid Anal Prev. 89:111-117.
[PDF-538.93 KB]
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Alternative Title:Accid Anal Prev
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Description: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.
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Pubmed ID:26851618
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Pubmed Central ID:PMC5119271
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Volume:89
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