Highlights We developed a new injury taxonomy for use with either ICD-10 or AIS08 recorded data. Using data mining we identified 77 patterns of co-occurring injuries in road users. Of these, 16 injury patterns were solely associated to a particular type of road user.

    Abstract In many road crashes the human body is exposed to high forces, commonly resulting in multiple injuries. This study of linked road crash data aimed to identify co-occurring injuries in multiple injured road users by using a novel application of a data mining technique commonly used in Market Basket Analysis. We expected that some injuries are statistically associated with each other and form Individual-Based Injury Patterns (IBIPs) and further that specific road users are associated with certain IBIPs. First, a new injury taxonomy was developed through a four-step process to allow the use of injury data recorded from either of the two major dictionaries used to document anatomical injury. Then data from the Swedish Traffic Accident Data Acquisition, which includes crash circumstances from the police and injury information from hospitals, was analysed for the years 2011 to 2017. The injury data was analysed using the Apriori algorithm to identify statistical association between injuries (IBIP). Each IBIP were then used as the outcome variable in logistic regression modelling to identify associations between specific road user types and IBIPs. A total of 48,544 individuals were included in the analysis of which 36,480 (75.1%) had a single injury category recorded and 12,064 (24.9%) were considered multiply injured. The data mining analysis identified 77 IBIPs in the multiply injured sample and 16 of these were associated with only one road user type. IBIPs and their relation to road user type are one step on the journey towards developing a tool to better understand and quantify injury severity and thereby improve the evidence-base supporting prioritisation of road safety countermeasures.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Identifying individual-based injury patterns in multi-trauma road users by using an association rule mining method


    Contributors:


    Publication date :

    2021-11-05




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Association rule mining for identification of port state control patterns in Malaysian ports

    Osman, Mohd Tarmizi / Yuli, Chen / Li, Tian et al. | Taylor & Francis Verlag | 2021

    Free access

    SARM - Succinct Association Rule Mining: An Approach to Enhance Association Mining

    Deogun, J. / Jiang, L. | British Library Conference Proceedings | 2005



    Single-Vehicle Run-Off Road Crashes Because of Cellphone Distraction: Finding Patterns with Rule Mining

    Rahman, M. Ashifur / Das, Subasish / Sun, Xiaoduan | Transportation Research Record | 2022