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医疗实体在低资源环境中与无微调的LLM进行连接

Suteera Seeha¹, Martin Boeker¹, Luise Modersohn¹

¹Chair of Medical Informatics, Institute of AI and Informatics in Medicine (AIIM), TUM University Hospital, Technical University of Munich, Munich, Germany.

Studies in health technology and informatics

|September 3, 2025

概括

大型语言模型 (LLM) 对医疗实体链接有希望,在没有微调的情况下改善候选生成和清晰度. 这种方法为传统方法提供了一个轻量级的替代方案.

科学领域:

生物医学自然语言处理
人工智能
医疗信息学

背景情况:

医疗实体链接将文本提及与标准化本体概念对齐.
现有的方法通常需要大量的数据和计算资源.
监督模型和特定领域的嵌入是常见的,但资源密集型.

研究的目的:

调查医疗实体链接的大型语言模型 (LLM).
通过同义词扩展和上下文学习使用LLM来增强候选人生成和清晰度.
评估基于LLM的方法与传统的字符串匹配和监督方法.

主要方法:

结合字符串匹配与LLM使用上下文学习,避免微调.
扩展提及与LLM生成的候选人生成的同义词.
使用LLM与少数镜头提示实体清晰度.

主要成果:

在MedMentions数据集上实现了56%的链接准确度,超过了字符串匹配.
候选生成达到70%的回忆率;明确性达到80%的准确性.
由LLM生成的描述并没有持续地提高准确性.

结论:

在低资源的场景中,LLM显示了医疗实体链接的潜力.
拟议的方法是监督模型的轻量级,无微调的替代方案.
这种方法可以适应生物医学以外的其他领域和本体学.

关键词:

统一的医疗语言系统生物医学本体学临床编码实体链接大型语言模型医疗实体链接

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