Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL

TL;DR

This paper demonstrates that using a business context document with GPT-4 significantly improves the accuracy of converting natural language queries into SQL for pharmacovigilance databases, enhancing data retrieval efficiency.

Contribution

The study introduces a novel retrieval-augmented generation framework that leverages contextual knowledge to improve LLM-based NLQ-to-SQL conversion accuracy in pharmacovigilance.

Findings

NLQ-to-SQL accuracy increased from 8.3% to 78.3% with context.

Performance remained high across different query complexities.

Contextual knowledge is critical for accurate SQL generation.

Abstract

Objective: To enhance the efficiency and 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 syntactically precise SQL queries. Each NLQ was presented to the LLM randomly and independently to prevent memorization. The study was conducted in three 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 and contextually relevant SQL queries. 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 and efficiency of information retrieval for non-technical users.