ArticleInjury prevention : journal of the International Society for Child and Adolescent Injury Prevention2016
Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.
Article in Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting occupational injury causal factors using text-based analytics: A systematic review.Frontiers in public health · 2022Pooled it
- Detecting PPE concerns in OSHA complaints using machine learning to support infectious disease outbreak response.Journal of occupational and environmental hygiene · 2026Article
- AI powered patient records analysis for injury surveillance in children and adolescents - a feasibility study from Switzerland.International journal of public health · 2026Article
- Application of Machine Learning to Auto-Code Injury Data in the e-CHIRPP System: Development and Evaluation Study.Online journal of public health informatics · 2025Article
- A machine learning approach to identify stride characteristics predictive of musculoskeletal injury, enforced rest and retirement in Thoroughbred racehorses.Scientific reports · 2024Article
- Estimating occupation-related crashes in light and medium size vehicles in Kentucky: A text mining and data linkage approach.Accident; analysis and prevention · 2024Article
- Establishment-level occupational safety analytics: Challenges and opportunities.International journal of industrial ergonomics · 2023Article
- Building Infrastructure for Surveillance of Adverse and Positive Childhood Experiences: Integrated, Multimethod Approaches to Generate Data for Prevention Action.American journal of preventive medicine · 2022Article
- Application of a Machine Learning-Based Decision Support Tool to Improve an Injury Surveillance System Workflow.Applied clinical informatics · 2022Article
- Advancing safety analytics: A diagnostic framework for assessing system readiness within occupational safety and health.Safety science · 2022Article
- Identifying intentional injuries among children and adolescents based on Machine Learning.PloS one · 2021Article
- Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.Journal of occupational and environmental medicine · 2018Article
- A qualitative exploration of work-related head injury: vulnerability at the intersection of workers' decision making and organizational values.BMC public health · 2017Article
- Injury surveillance: the next generation.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveVast 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.
methodsThis 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.
resultsThe 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.
conclusionsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.