title: Computerized "Learn-As-You-Go" classification of traumatic brain injuries using NEISS narrative data.
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.
authors: Research Information Solutions and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
authors: Wei
authors: W
authors: Chen
authors: 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.
authors: Krista K
authors: KK
authors: Wheeler
authors: Research Information Solutions and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
authors: Simon
authors: S
authors: Lin
authors: Research Information Solutions and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
authors: Yungui
authors: Y
authors: Huang
authors: 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.
authors: Huiyun
authors: H
authors: Xiang
mesh_terms: D001265:Athletic Injuries / Q000145:classification*; D001930:Brain Injuries / Q000145:classification* / Q000209:etiology; D016208:Databases, Factual; D006801:Humans; D000069550:Machine Learning; D008962:Models, Theoretical; D014481:United States
keywords: Computerized classification; Machine learning; Sports injury classification; Traumatic brain injuries; Unstructured NEISS narratives