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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-08-24

AUTHORS

Rave Harpaz, Alison Callahan, Suzanne Tamang, Yen Low, David Odgers, Sam Finlayson, Kenneth Jung, Paea LePendu, Nigam H. 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. More... »

PAGES

777-790

References to SciGraph publications

  • 2013-03-04. Pharmacovigilance Using Clinical Notes in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2012-05-02. Novel Data‐Mining Methodologies for Adverse Drug Event Discovery and Analysis in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2011-05-25. Detecting Drug Interactions From Adverse‐Event Reports: Interaction Between Paroxetine and Pravastatin Increases Blood Glucose Levels in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2012-06-20. Detection of Pharmacovigilance‐Related Adverse Events Using Electronic Health Records and Automated Methods in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2014-04-08. Toward Enhanced Pharmacovigilance Using Patient-Generated Data on the Internet in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2013-02-11. Performance of Pharmacovigilance Signal‐Detection Algorithms for the FDA Adverse Event Reporting System in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2014-01-15. Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection in BMC BIOINFORMATICS
  • 2013. AZDrugMiner: An Information Extraction System for Mining Patient-Reported Adverse Drug Events in Online Patient Forums in SMART HEALTH
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  • 2013. Mining Twitter Data for Potential Drug Effects in ADVANCED DATA MINING AND APPLICATIONS
  • 2002-05. Use of Screening Algorithms and Computer Systems to Efficiently Signal Higher-Than-Expected Combinations of Drugs and Events in the US FDA’s Spontaneous Reports Database in DRUG SAFETY
  • 2013-11-07. Natural Language Processing in Health Care and Biomedicine in BIOMEDICAL INFORMATICS
  • 2011-04. Social Media and Networks in Pharmacovigilance in DRUG SAFETY
  • 2013-06. Advancing the Science of Pharmacovigilance in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2014-07-02. Bridging Islands of Information to Establish an Integrated Knowledge Base of Drugs and Health Outcomes of Interest in DRUG SAFETY
  • 2011-05-17. Integration and publication of heterogeneous text-mined relationships on the Semantic Web in JOURNAL OF BIOMEDICAL SEMANTICS
  • 2012-11-23. A Reference Standard for Evaluation of Methods for Drug Safety Signal Detection Using Electronic Healthcare Record Databases in DRUG SAFETY
  • 2013-10-29. Defining a Reference Set to Support Methodological Research in Drug Safety in DRUG SAFETY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s40264-014-0218-z

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