Predicting patient presentation rates at mass gatherings using machine learning

Peter Serwylo, Paul Arbon, Grace Rumantir

    Research output: Contribution to conferencePaper

    5 Citations (Scopus)

    Abstract

    Mass gatherings have been defined as events where more than 1,000 people are present for a defined period of time. Such an event presents specific challenges with respect to medical care. First aid is provisioned on-site at most events in order to prevent undue strain on the local emergency services. In order to allocate enough resources to deal with the expected injuries, it is important to be able to accurately predict patient volumes. This study used machine learning techniques to identify which variables are the most important in predicting patient volumes at mass gatherings. Data from 201 mass gatherings across Australia was analysed, finding that event type is the most predictive variable, followed by the state or territory, heat index, humidity, whether it is bounded, and the time of day. Variables with little bearing on the outcome included the presence of alcohol, whether the event was indoors or outdoors, and whether it had one point of focus. The best predictive models produced acceptable predictions of the patient presentations 80% of the time, and this could be further improved using optimization techniques.

    Original languageEnglish
    Publication statusPublished - 1 Jan 2011
    Event8th International Conference on Information Systems for Crisis Response and Management (ISCRAM) 2011 -
    Duration: 8 May 2011 → …

    Conference

    Conference8th International Conference on Information Systems for Crisis Response and Management (ISCRAM) 2011
    Period8/05/11 → …

    Keywords

    • Data mining
    • Machine learning
    • Mass gathering
    • Patient presentation rate

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  • Cite this

    Serwylo, P., Arbon, P., & Rumantir, G. (2011). Predicting patient presentation rates at mass gatherings using machine learning. Paper presented at 8th International Conference on Information Systems for Crisis Response and Management (ISCRAM) 2011, .