Files and links (1)
Published, Version of Record (VoR)Open Access (License Unspecified), Open
Journal article
Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance
Injury prevention, v 22(Suppl 1)
01 Apr 2016
PMID: 26728004
Featured in Collection : UN Sustainable Development Goals @ Drexel
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 semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database.
Conclusions The last 20years 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.
Metrics
Details
- Title
- Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance
- Creators
- Kirsten Vallmuur - Queensland University of TechnologyHelen R. Marucci-Wellman - Liberty MutualJennifer A. Taylor - Drexel UniversityMark Lehto - Purdue University West LafayetteHelen L. Corns - Liberty MutualGordon S. Smith - University of Maryland, Baltimore
- Publication Details
- Injury prevention, v 22(Suppl 1)
- Publisher
- Bmj Publishing Group
- Number of pages
- 9
- Grant note
- R01AA18707 / US National Institute on Alcohol Abuse and Alcoholism; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Alcohol Abuse & Alcoholism (NIAAA) FT120100202 / Australian Research Council Future Fellowship; Australian Research Council R01AA018707 / NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Alcohol Abuse & Alcoholism (NIAAA)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Environmental and Occupational Health
- Web of Science ID
- WOS:000375274100008
- Scopus ID
- 2-s2.0-84962760902
- Other Identifier
- 991019169791104721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health