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Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of

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Automating adverse event (AE) extraction from FDA drug labels using AskFDALabel significantly improves efficiency and accuracy. This LLM-powered framework aids drug safety studies by reliably identifying toxicities like DILI and DICT.

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Area of Science:

  • Pharmacovigilance and Drug Safety
  • Artificial Intelligence in Healthcare
  • Natural Language Processing for Biomedical Text Mining

Background:

  • Adverse events (AEs) pose a significant public health risk, necessitating efficient methods for drug safety assessment.
  • US Food and Drug Administration (FDA) drug labeling documents are crucial for AE identification but manual extraction is resource-intensive and difficult to maintain.
  • Automating AE data extraction from these dynamic documents is essential for timely drug safety surveillance.

Purpose of the Study:

  • To develop and demonstrate an automated workflow, AskFDALabel, for extracting adverse event data from FDA drug labeling documents.
  • To leverage a large language model (LLM) with retrieval-augmented generation (RAG) for enhanced AE data extraction.
  • To evaluate the framework's performance in key drug safety classification tasks.

Main Methods:

  • Developed AskFDALabel, an LLM-powered framework incorporating a RAG component for querying the FDALabel database.
  • Implemented a workflow involving task-specific template selection, database querying, and LLM content preparation.
  • Evaluated performance on drug-induced liver injury (DILI) classification, drug-induced cardiotoxicity (DICT) classification, and AE term recognition.

Main Results:

  • AskFDALabel achieved high F1-scores: 0.978 for DILI, 0.931 for DICT, and 0.911 for AE annotation.
  • The framework outperformed traditional methods in AE data extraction and classification tasks.
  • Provided cited labeling content and explanations, supporting manual verification and enhancing transparency.

Conclusions:

  • AskFDALabel demonstrates high consistency with human AE annotation, particularly for DILI and DICT.
  • The framework significantly enhances the efficiency and accuracy of adverse event annotation.
  • Offers promising potential for advanced AE surveillance and comprehensive drug safety research.