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Drug Safety

, Volume 37, Issue 10, pp 777–790 | Cite as

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

  • Rave Harpaz
  • Alison Callahan
  • Suzanne Tamang
  • Yen Low
  • David Odgers
  • Sam Finlayson
  • Kenneth Jung
  • Paea LePendu
  • Nigam H. Shah
Leading Article
First Online:

Abstract

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources—such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs—that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

Keywords

Natural Language Processing Adverse Drug Event Text Mining Unify Medical Language System Name Entity Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

The writing of this manuscript was supported by National Institutes of Health (NIH) Grant U54-HG004028 for the National Center for Biomedical Ontology, and by National Institute of General Medical Sciences (NIGMS) grant GM101430-01A1.

Conflicts of interest

Nigam H. Shah is a Science Advisor to Apixio Inc. (www.apixio.com), and Kyron Inc. (www.kyron.com). Rave Harpaz is an employee of Oracle Health Sciences. Rave Harpaz, Alison Callahan, Suzanne Tamang, Yen Low, David Odgers, Sam Finlayson, Kenneth Jung, Paea LePendu, and Nigam H. Shah have no other conflicts of interest that are directly relevant to the content of this article.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rave Harpaz
    • 1
  • Alison Callahan
    • 1
  • Suzanne Tamang
    • 1
  • Yen Low
    • 1
  • David Odgers
    • 1
  • Sam Finlayson
    • 1
  • Kenneth Jung
    • 1
  • Paea LePendu
    • 1
  • Nigam H. Shah
    • 1
  1. 1.Center for Biomedical Informatics ResearchStanford UniversityStanfordUSA

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