Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.

Journal: Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
PMID: 26728004

Abstract

OBJECTIVE: Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance.

Authors

  • Kirsten Vallmuur
  • Helen R Marucci-Wellman
    Center for Injury Epidemiology, Liberty Mutual Research Institute for Safety, Hopkinton, Massachusetts, USA.
  • Jennifer A Taylor
    Department of Environmental & Occupational Health, School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA.
  • Mark Lehto
    School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA.
  • Helen L Corns
    Center for Injury Epidemiology, Liberty Mutual Research Institute for Safety, Hopkinton, Massachusetts, USA.
  • Gordon S Smith
    National Center for Trauma and EMS, University of Maryland School of Medicine, Baltimore, Maryland, USA.

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