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Pharmacovigilance Using Textual Data: The Need to Go Deeper and Wider into the Con(text)

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  • Tavpritesh Sethi

    (Indraprastha Institute of Information Technology
    Stanford Center for Biomedical Informatics Research)

  • Nigam H. Shah

    (Stanford Center for Biomedical Informatics Research)

Abstract

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Suggested Citation

  • Tavpritesh Sethi & Nigam H. Shah, 2017. "Pharmacovigilance Using Textual Data: The Need to Go Deeper and Wider into the Con(text)," Drug Safety, Springer, vol. 40(11), pages 1047-1048, November.
    • Handle: RePEc:spr:drugsa:v:40:y:2017:i:11:d:10.1007_s40264-017-0585-3
    DOI: 10.1007/s40264-017-0585-3
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    References listed on IDEAS

    as
    1. Yuan Luo & William K. Thompson & Timothy M. Herr & Zexian Zeng & Mark A. Berendsen & Siddhartha R. Jonnalagadda & Matthew B. Carson & Justin Starren, 2017. "Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review," Drug Safety, Springer, vol. 40(11), pages 1075-1089, November.
    2. Rave Harpaz & Alison Callahan & Suzanne Tamang & Yen Low & David Odgers & Sam Finlayson & Kenneth Jung & Paea LePendu & Nigam Shah, 2014. "Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art," Drug Safety, Springer, vol. 37(10), pages 777-790, October.
    Full references (including those not matched with items on IDEAS)

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