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Improving drug safety with adverse event detection using natural language processing.

Taxiarchis Botsis1, Kory Kreimeyer1

  • 1Department of Oncology, the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Expert Opinion on Drug Safety
|June 20, 2023
PubMed
Summary
This summary is machine-generated.

Natural language processing (NLP) advances drug safety by extracting data from text. However, real-world clinical deployment of these pharmacovigilance (PV) tools remains rare, needing better integration and standardized data models.

Keywords:
Adverse Drug EventDrug SafetyNatural Language ProcessingPharmacovigilancePostmarketing Surveillance

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Area of Science:

  • Pharmacovigilance
  • Natural Language Processing
  • Drug Safety

Background:

  • Pharmacovigilance (PV) relies on diverse data, often in free-text formats.
  • Natural Language Processing (NLP) can extract crucial information from these texts.
  • NLP aids in making informed drug safety decisions.

Purpose of the Study:

  • To review the application of NLP in drug safety.
  • To provide expert opinion on NLP in pharmacovigilance.

Main Methods:

  • Non-systematic literature review.
  • PubMed database queried for NLP in drug safety.
  • Findings distilled for expert opinion.

Main Results:

  • NLP techniques are increasingly applied to drug safety.
  • Fully deployed clinical NLP systems for PV are rare.
  • Little evidence of extracted data in standardized models.

Conclusions:

  • Real-world NLP implementation requires stakeholder engagement and workflow revision.
  • Standardized data models are needed for NLP portability in pharmacovigilance.
  • Bridging the gap between NLP techniques and clinical practice is essential.