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Pharmacovigilance

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Application of a Language Model Tool for COVID-19 Vaccine Adverse Event Monitoring Using Web and Social Media

Chathuri Daluwatte1, Alena Khromava2, Yuning Chen1

  • 1Digital Data, Sanofi, Cambridge, MA, United States.

JMIR Infodemiology
|December 20, 2024
PubMed
Summary

Social media monitoring can detect adverse events (AEs) related to COVID-19 vaccines faster than traditional systems. This approach enhances vaccine safety surveillance and public confidence.

Keywords:
COVID-19adverse eventdetectionlarge language modelmass vaccinationnatural language processingpharmacovigilancesafetysocial mediavaccine

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Area of Science:

  • Pharmacovigilance and Public Health Surveillance
  • Computational Linguistics and Natural Language Processing
  • Vaccine Safety Research

Background:

  • Traditional spontaneous reporting systems for vaccine adverse events (AEs) have significant time lags.
  • Global COVID-19 vaccination campaigns necessitate real-time monitoring of potential AEs.
  • Social media and web content offer opportunities for faster AE detection.

Purpose of the Study:

  • To monitor adverse events (AEs) associated with COVID-19 vaccines using social media and online support groups.
  • To leverage medical context-aware natural language processing (NLP) language models for AE detection.
  • To augment traditional pharmacovigilance data with real-time social media insights.

Main Methods:

  • Developed a web application analyzing global social media, blogs, and forums in 61 languages for COVID-19 vaccine keywords.
  • Utilized a PubmedBERT-based named-entity recognition model for lay language AE identification (precision=0.76, recall=0.82).
  • Mapped identified AEs to Medical Dictionary for Regulatory Activities (MedDRA) terms using knowledge graphs and presented findings via visual analytics.

Main Results:

  • The majority of detected AEs occurred in 2021, with a decrease in 2022.
  • AEs identified through the web app aligned with those reported by health authorities during similar periods.
  • The system demonstrated consistency with established safety reporting channels.

Conclusions:

  • Web and social media monitoring can facilitate the early detection of potential vaccine-related adverse events (AEs).
  • This approach enhances post-marketing surveillance by providing a supplementary data source for signal detection.
  • Utilizing social media data can improve communication with stakeholders and bolster confidence in vaccine safety monitoring systems.