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Detecting Anomalies in Controlled Drug Prescription Data Using Probabilistic Models

  • Conference paper
Book cover Artificial Life and Computational Intelligence (ACALCI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8955))

Abstract

Opioid analgesic drugs are widely used in pain management and substance dependence treatment. However, these drugs have high potential for misuse and subsequent harm. As a result, their prescribing is monitored and controlled. In Queensland, Australia, the Medicines Regulation and Quality Unit within the state health system maintains a database of prescribing events and uses this data to identify anomalies and provide subsequent support for patients and prescribers. In this study, we consider this task as an unsupervised anomaly detection problem. We use probability density estimation models to describe the distribution of the data over a number of key attributes and use the model to identify anomalies as points with low estimated probability. The results are validated against cases identified by healthcare domain experts. There was strong agreement between cases identified by the models and expert clinical assessment.

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Author information

Authors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Xuelei Hu, Marcus Gallagher & Janet Wiles

  2. Medicines Regulation and Quality Unit, Queensland Health, Brisbane, Australia

    William Loveday

  3. Discipline of Psychiatry, The University of Queensland, Brisbane, Australia

    Jason P. Connor

  4. Centre for Youth Substance Abuse Research, The University of Queensland, Brisbane, Australia

    Jason P. Connor

Authors
  1. Xuelei Hu
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  2. Marcus Gallagher
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  3. William Loveday
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  4. Jason P. Connor
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  5. Janet Wiles
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Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computer Science, The University of Newcastle, 2308, Callaghan, NSW, Australia

    Stephan K. Chalup

  2. School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, NSW, Australia

    Alan D. Blair

  3. Faculty of Business, Bond University, 4226, Robina, QLD, Australia

    Marcus Randall

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© 2015 Springer International Publishing Switzerland

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Hu, X., Gallagher, M., Loveday, W., Connor, J.P., Wiles, J. (2015). Detecting Anomalies in Controlled Drug Prescription Data Using Probabilistic Models. In: Chalup, S.K., Blair, A.D., Randall, M. (eds) Artificial Life and Computational Intelligence. ACALCI 2015. Lecture Notes in Computer Science(), vol 8955. Springer, Cham. https://doi.org/10.1007/978-3-319-14803-8_26

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  • DOI: https://doi.org/10.1007/978-3-319-14803-8_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14802-1

  • Online ISBN: 978-3-319-14803-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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