Computerized "Learn-As-You-Go" classification of traumatic brain injuries using NEISS narrative data. (April 2016) - Chen, Wei; Wheeler, Krista K.; Lin, Simon; Huang, Yungui; Xiang, Huiyun
Highlights: Manual classification of NEISS injury narratives has been traditionally very labor-intensive. Batch learning systems are hard to use by non-technical injury researchers. The "Learn-As-You-Go" DUALIST system used in this study represents an interactive yet effective way of machine learning-based injury classification. 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.