Pharmacovigilance (PV) is essential for drug safety, primarily focusing on adverse event monitoring. Traditionally, accessing safety data required database expertise, limiting broader use. This paper introduces a novel application of Large Language Models (LLMs) to democratize database access for non-technical users. Utilizing OpenAI's GPT-4, we developed a chatbot that generates structured query language (SQL) queries from natural language, bridging the gap between domain knowledge and technical requirements. The proposed application aims for more inclusive and efficient data access, enhancing decision making in drug safety. By providing LLMs with plain language summaries of expert knowledge, our approach significantly improves query accuracy over methods relying solely on database schemas. The application of LLMs in this context not only optimizes PV data analysis, ensuring timely and precise drug safety reporting -- a crucial component in adverse drug reaction monitoring -- but also promotes safer pharmacological practices and informed decision making across various data intensive fields.

翻译：药物警戒对保障用药安全至关重要，其核心在于不良事件监测。传统上，安全数据访问需要数据库专业知识，限制了更广泛的应用。本文提出大语言模型在数据库访问民主化方面的新应用，使非技术用户能够便捷获取数据。利用OpenAI的GPT-4，我们开发了能够将自然语言转换为结构化查询语言指令的聊天机器人，从而弥合领域知识与技术需求之间的鸿沟。该应用致力于实现更包容、高效的数据访问机制，以提升药物安全领域的决策质量。通过向大语言模型提供专家知识的自然语言摘要，我们的方法相较于仅依赖数据库架构的方案，显著提高了查询准确率。大语言模型在此场景中的应用不仅优化了药物警戒数据分析——确保及时精准的药品安全报告（这是药物不良反应监测的关键环节），还促进了更安全的药事实践，并为各类数据密集型领域的科学决策提供支持。