Preventive product safety – monitoring accidental injuries related to consumer products in the European Union

Bauer, Robert; Sector, Mathilde J.

doi:10.1076/icsp.10.4.253.16784

Cited by 2 publications

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“…This is consistent with the findings from Jones and Lyons’s study in which text narrative data was found to reduce the proportion of unknown cases for object involved [ 10 ]. The results in this study also confirmed the findings from Bauer and Sector’s study that found that a PIF categorisation tool is useful to classify product-related cases in injury data [ 9 ].…”

Section: Discussionsupporting

confidence: 89%

“…In 2000, a categorisation system to determine product involvement was developed in a European study conducted by Bauer and Sector from the Institute Sicher Leben (Austrian Institute for Home and Leisure Safety). Called the Product Involvement Factor (PIF), it is a tool that has been used in the European injury surveillance system (formerly EHLASS) to interrogate text narrative data by using an automated keyword matching method to group injury cases into seven “involvement” categories [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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The Extent of Consumer Product Involvement in Paediatric Injuries

Catchpoole

Walker

Vallmuur

2016

IJERPH

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A challenge in utilising health sector injury data for Product Safety purposes is that clinically coded data have limited ability to inform regulators about product involvement in injury events, given data entry is bound by a predefined set of codes. Text narratives collected in emergency departments can potentially address this limitation by providing relevant product information with additional accompanying context. This study aims to identify and quantify consumer product involvement in paediatric injuries recorded in emergency department-based injury surveillance data. A total of 7743 paediatric injuries were randomly selected from Queensland Injury Surveillance Unit database and associated text narratives were manually reviewed to determine product involvement in the injury event. A Product Involvement Factor classification system was used to categorise these injury cases. Overall, 44% of all reviewed cases were associated with consumer products, with proximity factor (25%) being identified as the most common involvement of a product in an injury event. Only 6% were established as being directly due to the product. The study highlights the importance of utilising injury data to inform product safety initiatives where text narratives can be used to identify the type and involvement of products in injury cases.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

The Extent of Consumer Product Involvement in Paediatric Injuries

Catchpoole

Walker

Vallmuur

2016

IJERPH

View full text Add to dashboard Cite

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

et al. 2016

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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.

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Preventive product safety – monitoring accidental injuries related to consumer products in the European Union

Cited by 2 publications

References 0 publications

The Extent of Consumer Product Involvement in Paediatric Injuries

The Extent of Consumer Product Involvement in Paediatric Injuries

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

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