@article{ref1,
title="Computerized "Learn-As-You-Go" classification of traumatic brain injuries using NEISS narrative data",
journal="Accident analysis and prevention",
year="2016",
author="Chen, Wenxue and Wheeler, Krista K. and Lin, Simon and Huang, Yungui and Xiang, Huiyun",
volume="89",
number="",
pages="111-117",
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 &quot;Learn-As-You-Go&quot; 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 &quot;Learn-As-You-Go&quot; 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.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2016.01.012",
url="http://dx.doi.org/10.1016/j.aap.2016.01.012"
}