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

Objective: To enhance the accuracy of information retrieval from pharmacovigilance (PV) databases by employing Large Language Models (LLMs) to convert natural language queries (NLQs) into Structured Query Language (SQL) queries, leveraging a business context document.

Materials and methods: We utilized OpenAI's GPT-4 model within a retrieval-augmented generation (RAG) framework, enriched with a business context document, to transform NLQs into executable SQL queries. Each NLQ was presented to the LLM randomly and independently to prevent memorization. The study was conducted in 3 phases, varying query complexity, and assessing the LLM's performance both with and without the business context document.

Results: Our approach significantly improved NLQ-to-SQL accuracy, increasing from 8.3% with the database schema alone to 78.3% with the business context document. This enhancement was consistent across low, medium, and high complexity queries, indicating the critical role of contextual knowledge in query generation.

Discussion: The integration of a business context document markedly improved the LLM's ability to generate accurate SQL queries (ie, both executable and returning semantically appropriate results). Performance achieved a maximum of 85% when high complexity queries are excluded, suggesting promise for routine deployment.

Conclusion: This study presents a novel approach to employing LLMs for safety data retrieval and analysis, demonstrating significant advancements in query generation accuracy. The methodology offers a framework applicable to various data-intensive domains, enhancing the accessibility of information retrieval for non-technical users.