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Identifying individual-based injury patterns in multi-trauma road users by using an association rule mining method
Helen Fagerlind1, Lara Harvey2, Peter Humburg3
1Neuroscience Research Australia, Sydney, NSW 2031, Australia; School of Population Health, University of New South Wales, Sydney, NSW 2052, Australia; Division of Vehicle Safety, Chalmers University of Technology, 412 96 Gothenburg, Sweden.
Accident; Analysis and Prevention
|November 14, 2021
Summary
This study used data mining to find linked injuries in road crash victims. It identified 77 injury patterns, with 16 linked to specific road user types, aiding road safety improvements.
Area of Science:
- Road safety research
- Injury epidemiology
- Data mining applications
Background:
- Road crashes frequently cause multiple injuries due to high forces.
- Understanding co-occurring injuries is crucial for effective road safety interventions.
Purpose of the Study:
- To identify statistically associated injuries (Individual-Based Injury Patterns - IBIPs) in multiply injured road users.
- To determine associations between specific road user types and identified IBIPs.
Main Methods:
- Developed a novel injury taxonomy for standardized documentation.
- Applied the Apriori data mining algorithm to linked Swedish road crash data (2011-2017).
- Utilized logistic regression to link IBIPs with road user types.
Main Results:
- Analyzed 48,544 individuals; 24.9% were multiply injured.
- Identified 77 distinct IBIPs among multiply injured individuals.
- Found 16 IBIPs significantly associated with specific road user types.
Conclusions:
- Data mining effectively reveals co-occurring injury patterns in road trauma.
- Linking injury patterns to road user types offers insights for targeted safety measures.
- This approach supports developing tools for better injury severity quantification and road safety prioritization.
