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

Author

Listed:
  • Rave Harpaz
  • Alison Callahan
  • Suzanne Tamang
  • Yen Low
  • David Odgers
  • Sam Finlayson
  • Kenneth Jung
  • Paea LePendu
  • Nigam Shah

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. Copyright Springer International Publishing Switzerland 2014

Suggested Citation

  • 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.
    • Handle: RePEc:spr:drugsa:v:37:y:2014:i:10:p:777-790
    DOI: 10.1007/s40264-014-0218-z
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    Cited by:

    1. Yiqing Zhao & Yue Yu & Hanyin Wang & Yikuan Li & Yu Deng & Guoqian Jiang & Yuan Luo, 2022. "Machine Learning in Causal Inference: Application in Pharmacovigilance," Drug Safety, Springer, vol. 45(5), pages 459-476, May.
    2. Galit Klein & Eyal Eckhaus, 2017. "Sensemaking and sensegiving as predicting organizational crisis," Risk Management, Palgrave Macmillan, vol. 19(3), pages 225-244, August.
    3. 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.
    4. 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.
    5. Susan Colilla & Elad Yom Tov & Ling Zhang & Marie-Laure Kurzinger & Stephanie Tcherny-Lessenot & Catherine Penfornis & Shang Jen & Danny S. Gonzalez & Patrick Caubel & Susan Welsh & Juhaeri Juhaeri, 2017. "Validation of New Signal Detection Methods for Web Query Log Data Compared to Signal Detection Algorithms Used With FAERS," Drug Safety, Springer, vol. 40(5), pages 399-408, May.
    6. Eyal Eckhaus & Zachary Sheaffer, 2018. "Managerial hubris detection: the case of Enron," Risk Management, Palgrave Macmillan, vol. 20(4), pages 304-325, November.
    7. Lucie M. Gattepaille & Sara Hedfors Vidlin & Tomas Bergvall & Carrie E. Pierce & Johan Ellenius, 2020. "Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project," Drug Safety, Springer, vol. 43(8), pages 797-808, August.

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