A Bayesian Network-Based Semi-automated Injury Classification System
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-10780-1_31
Reference24 articles.
1. Vallmuur K, Marucci-Wellman HR, Taylor JA, Lehto M, Corns HL, Smith GS (2016) Harnessing information from injury narratives in the ‘big data’ era: understanding and applying machine learning for injury surveillance. Inj Prev
2. McKenzie K, Enraght-Moony E, Harding L, Walker S, Waller G, Chen L (2008) Coding external causes of injuries: problems and solutions. Accid Anal Prev 40(2):714–718
3. Vallmuur K (2015) Machine learning approaches to analysing textual injury surveillance data: a systematic review. Accid Anal Prev
4. Wellman HM, Lehto MR, Sorock GS, Smith GS (2004) Computerized coding of injury narrative data from the National Health Interview Survey. Accid Anal Prev
5. Marucci-Wellman HR, Lehto MR, Corns HL (2015) A practical tool for public health surveillance: semi-automated coding of short injury narratives from large administrative databases using Naïve Bayes algorithms. Accid Anal Prev
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