Detecting and Understanding Dysfunctional Anomalies in Queensland Healthcare Databases (2008–2011)

Abstract:
Healthcare systems are large complex organisations that are required to function effectively and efficiently. They routinely produce a large amount of data that cannot be comprehensively analysed using manual procedures. This project will develop and apply state-of-the-art data mining and machine learning techniques to support the detection of anomalies in healthcare databases. Improvements in anomaly detection will lead to earlier detection and a better understanding of problems in the system. Techniques will be developed and deployed in case studies on data for hospital bed occupancy, cardiac units and emergency units in Queensland Health.
Grant type:
ARC Linkage Projects
Researchers:
  • Associate Professor
    School of Information Technology and Electrical Engineering
    Faculty of Engineering, Architecture and Information Technology
  • Director
    Centre for Youth Substance Abuse
    Faculty of Health and Behavioural Sciences
    Professor
    Princess Alexandra Hospital Southside Clinical Unit
    Faculty of Medicine
    Affiliate Professor
    School of Psychology
    Faculty of Health and Behavioural Sciences
  • Professor
    School of Information Technology and Electrical Engineering
    Faculty of Engineering, Architecture and Information Technology
Funded by:
Australian Research Council