Mining unexpected temporal associations: Applications in detecting adverse drug reactions

Huidong Jin*, Jin Chen, Hongxing He, Graham J. Williams, Chris Kelman, Christine M. O'Keefe

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    79 Citations (Scopus)

    Abstract

    In various real-world applications, it is very useful mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes, e.g., taking two medicines together sometimes causing an adverse reaction. These unanticipated episodes are usually unexpected and infrequent, which makes existing data mining techniques, mainly designed to find frequent patterns, ineffective. In this paper, we propose unexpected temporal association rules (UTARs) to describe them. To handle the unexpectedness, we introduce a new interestingness measure, residual-leverage, and develop a novel case-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle the infrequency, we develop a new algorithm MUTARC to find pairwise UTARs. The MUTARC is applied to generate adverse drug reaction (ADR) signals from real-world healthcare administrative databases. It reliably shortlists not only six known ADRs, but also another ADR, flucloxacillin possibly causing hepatitis, which our algorithm designers and experiment runners have not known before the experiments. The MUTARC performs much more effectively than existing techniques. This paper clearly illustrates the great potential along the new direction of ADR signal generation from healthcare administrative databases.

    Original languageEnglish
    Pages (from-to)488-500
    Number of pages13
    JournalIEEE Transactions on Information Technology in Biomedicine
    Volume12
    Issue number4
    DOIs
    Publication statusPublished - Jul 2008

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