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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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Monitoring Over-The-Counter Drug Misuse in Japanese User-Generated Data.

Tomohiro Nishiyama1, Shuntaro Yada2, Shoko Wakamiya1

  • 1Division of Information Science, Nara Institute of Science and Technology, Japan.

Studies in Health Technology and Informatics
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Summary
This summary is machine-generated.

This study developed a system to detect over-the-counter (OTC) drug misuse on social media, aiding public health by visualizing drug use trends.

Keywords:
Data VisualizationDrug MisuseMachine LearningNatural Language ProcessingPharmacovigilanceText Classification

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Area of Science:

  • Public Health
  • Computational Linguistics
  • Pharmacovigilance

Background:

  • Over-the-counter (OTC) drug misuse is a significant global public health issue.
  • Social media data offers a potential source for monitoring drug use patterns.

Purpose of the Study:

  • To develop and evaluate a system for detecting and visualizing inappropriate OTC drug use in social media data.
  • To address linguistic and cultural nuances in Japanese social media for accurate misuse detection.

Main Methods:

  • Constructed a labeled corpus of 20,036 Japanese tweets, including 7,000 medication-related posts.
  • Fine-tuned Japanese bidirectional encoder representations from transformers (BERT) models for misuse pattern identification (e.g., overuse).
  • Integrated a visualization tool to display temporal and categorical trends in OTC drug use.

Main Results:

  • The system demonstrated strong performance in identifying specific OTC drug misuse patterns.
  • Visualization tools provided insights into temporal and categorical trends of drug misuse.
  • Error analysis highlighted challenges with ambiguous terms and noise in social media data.

Conclusions:

  • The model showed effectiveness with sufficient data but faced limitations with underrepresented categories.
  • Improved contextual understanding and advanced techniques like larger language models or data augmentation are needed.
  • The system's approach to detecting OTC drug misuse and visualizing trends is promising for real-time pharmacovigilance and adaptable to other languages.