使用基础LLM和RAG自动提取IDMP数据模型的SmPC文档:用于制药监管事务的初步实验
Hocine Kadi1, Alaa Abdellatif1, Daniel Isaac Kemajou Njamen2
1Product Life Group, Courbevoie, France.
Frontiers in medicine
|August 29, 2025
概括
具有检索增强生成 (RAG) 的大型语言模型 (LLM) 可以准确地从药物文档中提取结构化数据. 这有助于监管协调和行业的数字化转型.
科学领域:
- 制药监管科学
- 药物开发中的人工智能
- 数据标准化和互操作性
背景情况:
- 制药行业正在向数据驱动的流程过渡,需要结构化数据交换标准,如药品识别 (IDMP).
- 从非结构化文件中提取和标准化数据,例如产品特性摘要 (SmPC),以符合IDMP的要求,存在重大挑战.
研究的目的:
- 研究基础大型语言模型 (LLM) 与检索增强生成 (RAG) 结合用于从SmPC文档中提取结构化数据的有效性.
- 评估不同的嵌入模型和提示策略,以优化数据提取准确性以达到IDMP合规性.
主要方法:
- 使用的基础LLM (Claude 3.5 索内特,Gemini 1.5 闪光灯) 与检索增强生成 (RAG).
- 采用了各种嵌入模型 (通用,专业,混合) 和基于规则的检索方法.
- 评估多种提示策略,重点关注上下文,行动和例子.
主要成果:
- 与双子1.5闪光相比,克劳德3.5闪光显示出更高的性能.
- 使用嵌入模型进行语义搜索的获取增强生成 (RAG) 优于基于规则的方法.
- 基于背景的,以行动为导向的,以示例为基础的提示更有效.
- 在药物数据提取方面达到0. 98的BERT F1得分.
结论:
- 通过LLM-RAG方法,可以准确且可扩展地从SmPC中提取结构化数据,从而支持药物监管的数字化转型.
- 这种方法促进了监管过程中的标准化,互操作性和协调性.
- 有效实施可以提高监管合规性,简化提交,并改善制药公司的数据一致性.
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