Citation

Chen W, Wheeler KK, Lin S, Huang Y, Xiang H. Accid. Anal. Prev. 2016; 89: 111-117.

Affiliation

Center for Injury Research and Policy, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA; Center for Pediatric Trauma Research, Nationwide Children's Hospital, Columbus, OH, USA. Electronic address: huiyun.xiang@nationwidechildrens.org.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.aap.2016.01.012

PMID

26851618

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.

Language: en