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Artificial Intelligent Context-Aware Machine-Learning Tool to Detect Adverse Drug Events from Social Media Platforms.

Don Roosan1, Anandi V Law2, Moom R Roosan3

  • 1Department of Pharmacy Practice and Administration, Western University of Health Sciences, 309 E 2nd St, Pomona, CA, 91766, USA. droosan@westernu.edu.

Journal of Medical Toxicology : Official Journal of the American College of Medical Toxicology
|September 13, 2022
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Summary
This summary is machine-generated.

A new AI tool, aTarantula, effectively detects adverse drug events (ADEs) from social media using context-aware machine learning. This approach enhances pharmacovigilance by analyzing patient-reported data for drug safety.

Keywords:
Adverse drug eventMachine learningNatural language processingPharmacovigilanceSocial media

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Area of Science:

  • Pharmacovigilance and Drug Safety
  • Artificial Intelligence in Healthcare
  • Natural Language Processing (NLP)

Background:

  • Pharmacovigilance (PV) relies on detecting post-marketing adverse drug events (ADEs).
  • Existing NLP tools struggle with context, limiting efficiency in extracting ADEs from unstructured text.
  • Social media platforms offer vast, real-world patient data for ADE detection.

Purpose of the Study:

  • To develop and validate aTarantula, an innovative NLP tool using context-aware machine learning.
  • To automatically detect warfarin-related ADEs from social media discussions.
  • To create an aggregated lexicon for mining ADEs from online patient forums.

Main Methods:

  • Utilized FastText embeddings and an aggregated lexicon derived from warfarin package inserts and databases (UMLS, FAERS).
  • Extracted contextual data from three patient forums (MedHelp, MedsChat, PatientInfo).
  • Refined and manually validated extracted data by three clinical pharmacists.

Main Results:

  • aTarantula achieved high performance with 84.2% sensitivity and 98% specificity, confirmed by clinical pharmacists.
  • Identified frequent ADEs in multiple organ systems (1.50%) and CNS side effects (1.19%).
  • Established a Spearman rank correlation of 0.19 between patient-reported data and FAERS.

Conclusions:

  • Successfully developed aTarantula, an AI-powered tool for automated ADE extraction from social media.
  • Demonstrated the feasibility of using aTarantula for detecting ADEs in real-world patient data.
  • Recommended further validation of aTarantula on diverse datasets for broader application.