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  1. Home
  2. Automating Pharmacovigilance Evidence Generation: Using Large Language Models To Produce Context-aware Structured Query Language.
  1. Home
  2. Automating Pharmacovigilance Evidence Generation: Using Large Language Models To Produce Context-aware Structured Query Language.

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Automating pharmacovigilance evidence generation: using large language models to produce context-aware structured

Jeffery L Painter1, Venkateswara Rao Chalamalasetti1,2, Raymond Kassekert3

  • 1GlaxoSmithKline, Durham, NC 27701, United States.

JAMIA Open
|February 10, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Large Language Models (LLMs) can now convert natural language queries into SQL for pharmacovigilance databases. Adding business context significantly boosted accuracy from 8.3% to 78.3%.

Keywords:
drug safetyinformation retrievallarge language models (LLMs)natural language processing (NLP)pharmacovigilance

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Area of Science:

  • Pharmacovigilance
  • Artificial Intelligence
  • Database Management

Background:

  • Pharmacovigilance databases require complex queries for safety data retrieval.
  • Natural Language Queries (NLQs) are often difficult to translate into Structured Query Language (SQL) for database interaction.
  • Large Language Models (LLMs) offer potential for automating NLQ-to-SQL conversion.

Purpose of the Study:

  • To enhance information retrieval accuracy in pharmacovigilance databases.
  • To develop a method for converting NLQs into SQL queries using LLMs.
  • To evaluate the impact of business context on LLM-driven query generation.

Main Methods:

  • Utilized OpenAI's GPT-4 model within a retrieval-augmented generation (RAG) framework.
  • Enriched the RAG framework with a business context document.
  • Assessed LLM performance across varying query complexities (low, medium, high) with and without the business context.
  • Main Results:

    • NLQ-to-SQL accuracy increased from 8.3% (schema only) to 78.3% with the business context document.
    • Accuracy improvements were consistent across all query complexity levels.
    • Excluding high complexity queries, performance reached up to 85%, indicating strong potential for deployment.

    Conclusions:

    • Integrating business context significantly improves LLM accuracy for generating executable and semantically correct SQL queries.
    • The proposed methodology enhances the accessibility of pharmacovigilance data for non-technical users.
    • This approach provides a transferable framework for improving data retrieval in various data-intensive fields.