Detecting anomalies in controlled drug prescription data using probabilistic models

Hu, Xuelei, Gallagher, Marcus, Loveday, William, Connor, Jason P. and Wiles, Janet (2015). Detecting anomalies in controlled drug prescription data using probabilistic models. In: Stephan K. Chalup, Alan D. Blair and Marcus Randall, Artificial Life and Computational Intelligence: First Australasian Conference, ACALCI 2015, Proceedings. 1st Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2015, Newcastle, NSW Australia, (337-349). 5 - 7 February 2015.


Author Hu, Xuelei
Gallagher, Marcus
Loveday, William
Connor, Jason P.
Wiles, Janet
Title of paper Detecting anomalies in controlled drug prescription data using probabilistic models
Conference name 1st Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2015
Conference location Newcastle, NSW Australia
Conference dates 5 - 7 February 2015
Proceedings title Artificial Life and Computational Intelligence: First Australasian Conference, ACALCI 2015, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Place of Publication Heidelberg, Germany
Publisher Springer Verlag
Publication Year 2015
Year available 2015
Sub-type Fully published paper
Open Access Status Not yet assessed
ISBN 9783319148021
9783319148038
ISSN 0302-9743
1611-3349
Editor Stephan K. Chalup
Alan D. Blair
Marcus Randall
Volume 8955
Start page 337
End page 349
Total pages 13
Chapter number 26
Total chapters 34
Language eng
Abstract/Summary 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.
Formatted Abstract/Summary
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.
Subjects 2614 Theoretical Computer Science
1700 Computer Science
Keyword Anomaly detection
Controlled drug
Prescription data
Probabilistic model
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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