The role of data mining in pharmacovigilance

Manfred Hauben, David Madigan, Charles M Gerrits, Louisa Walsh, Eugene P Van Puijenbroek

    Research output: Contribution to journalArticleAcademicpeer-review

    162 Citations (Scopus)


    A principle concern of pharmacovigilance is the timely detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of this process is the scientific acumen of the pharmacovigilance domain expert. There is understandably an interest in developing database screening tools to assist human reviewers in identifying associations worthy of further investigation (i.e., signals) embedded within a database consisting largely of background 'noise' containing reports of no substantial public health significance. Data mining algorithms are, therefore, being developed, tested and/or used by health authorities, pharmaceutical companies and academic researchers. After a focused review of postapproval drug safety signal detection, the authors explain how the currently used algorithms work and address key questions related to their validation, comparative performance, deployment in naturalistic pharmacovigilance settings, limitations and potential for misuse. Suggestions for further research and development are offered.

    Original languageEnglish
    Pages (from-to)929-948
    Number of pages20
    JournalExpert Opinion on Drug Safety
    Issue number5
    Publication statusPublished - Sep-2005


    • Adverse Drug Reaction Reporting Systems
    • Algorithms
    • Databases, Factual
    • Humans
    • Information Storage and Retrieval
    • Population Surveillance
    • Public Health

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