Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Vallmuur, Kirsten, Marucci-Wellman, Helen R., Taylor, Jennifer A., Lehto, Mark, Corns, Helen L., & Smith, Gordon S. (2016) Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance. Injury Prevention, 22, i34-i42.

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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.

Methods

This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach.

Results

The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database.

Conclusion

The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

Impact and interest:

1 citations in Scopus
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1 citations in Web of Science®

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ID Code: 91289
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: injury surveillance, machine learning, narrative text, coding
DOI: 10.1136/injuryprev-2015-041813
ISSN: 1353-8047
Subjects: Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700) > Health Information Systems (incl. Surveillance) (111711)
Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Psychology & Counselling
Funding:
Copyright Owner: Copyright 2015 The Author(s)
Deposited On: 17 Dec 2015 00:43
Last Modified: 16 May 2016 14:44

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