将语义检索和思维链推理集成到小语言模型中,用于SNOMED CT规范化
Pilar López-Úbeda1, Teodoro Martín-Noguerol2, Antonio Luna2
1NLP Department. HT Médica. Carmelo Torres N°2, 23007 Jaén, Spain.
International journal of medical informatics
|February 12, 2026
概括
小语言模型 (SLMs) 与检索增强生成 (RAG) 和思维链 (CoT) 结合,有效地将乳腺活检病理报告正常化为SNOMED CT形态代码. 该系统通过提供精心策划的代码列表来帮助病理学家,改善工作流程和数据准确性.
科学领域:
- 医疗信息学 医疗信息学
- 计算病理学计算病理学
- 自然语言处理 (Natural Language Processing) 是一种自然语言处理.
背景情况:
- 乳腺活检的病理学报告带来了相当大的工作量.
- 标准化编码系统,如SNOMED CT 形态代码对于数据的一致性和质量控制至关重要.
研究的目的:
- 评估帮助病理学家将自由文本病理学报告正常化为SNOMED CT形态代码的系统.
- 为有效的病理学家选择提供候选代码的简单列表.
主要方法:
- 利用了2,718份乳腺活检病理学报告.
- 开发了一个规范化管道,将小语言模型 (SLM) 与语义检索结合起来.
- 评估了三种策略:零射击提示,思维链 (CoT) 与检索增强生成 (RAG) 以及RAG与CoT结合.
主要成果:
- RAG + CoT策略表现出卓越的表现,获得了高的Hit@5分数 (例如,Gemma的72.11%).
- 这一策略显示了在排名1的正确代码的强集中.
- CoT + RAG的表现优于零射击提示,但正确代码的排名低于RAG + CoT.
结论:
- 将SLM与RAG和CoT集成,为编码乳腺活检报告提供了一个有效的工具.
- 该系统通过提供SNOMED CT代码的精选列表来提高临床工作流程和数据质量.
- 这便于前性和后性分析.
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