Mining unexpected associations for signalling potential adverse drug reactions from administrative health databases

Huidong Jin*, Jie Chen, Chris Kelman, Hongxing He, Damien McAullay, Christine M. O'Keefe

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    20 Citations (Scopus)

    Abstract

    Adverse reactions to drugs are a leading cause of hospitalisation and death worldwide. Most post-marketing Adverse Drug Reaction (ADR) detection techniques analyse spontaneous ADR reports which underestimate ADRs significantly. This paper aims to signal ADRs from administrative health databases in which data are collected routinely and are readily available. We introduce a new knowledge representation, Unexpected Temporal Association Rules (UTARs), to describe patterns characteristic of ADRs. Due to their unexpectedness and infrequency, existing techniques cannot perform effectively. To handle this unexpectedness we introduce a new interestingness measure, unexpected-leverage, and give a user-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle in-frequency, we develop a new algorithm, MUTARA, for mining simple UTARs. MUTARA effectively short-lists some known ADRs such as the disease esophagitis unexpectedly associated with the drug alendronate. Similarly, MUTARA signals atorvastatin followed by nizatidine or di-cloxacillin which may be prescribed to treat its side effects stomach ulcer or urinary tract infection, respectively. Compared with association mining techniques, MUTARA signals potential ADRs more effectively.

    Original languageEnglish
    Title of host publicationAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
    Pages867-876
    Number of pages10
    DOIs
    Publication statusPublished - 2006
    Event10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
    Duration: 9 Apr 200612 Apr 2006

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3918 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006
    Country/TerritorySingapore
    CitySingapore
    Period9/04/0612/04/06

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