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在ICF分类中评估LLM的表现:从医学和一般模型的见解

Feten Skhiri Gabbouj, Matei Mancas, Mathias Blandeau

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

本研究探讨使用大型语言模型 (LLM) 来自动化国际功能,残疾和健康分类 (ICF) 从医疗文本编码. 初步结果表明,专门的医学LLM在这个复杂的任务中可能不会超过一般的LLM.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
卫生分类系统卫生分类系统

背景情况:

非结构化的临床文本数据,包括患者的事和见解,在医疗保健中往往未得到充分利用.
大型语言模型 (LLM) 为处理和结构化这些复杂的医疗数据提供了潜力.
世界卫生组织的功能,残疾和健康国际分类 (ICF) 为描述健康和残疾提供了一个标准化的框架.

研究的目的:

调查医学上微调的LLM用于自动ICF分类的应用.
为了比较医疗LLM (MedAlpaca,Meditron) 与通用LLM (ChatGPT,Claude) 在处理真实医疗病例中的效率.
为了评估在康复和重症监护单位数据的背景下,专业与一般LLMs的表现.

主要方法:

使用医学上微调的LLM (MedAlpaca,Meditron) 和通用LLM (ChatGPT,Claude).使用医学上微调的LLM (MedAlpaca,Meditron) 和通用LLM (ChatGPT,Claude).
将LLM应用到来自康复和重症监护机构的真实医学病例数据.
对不同LLM用于自动ICF代码生成的性能进行了比较.

主要成果:

医学LLM显示了ICF分类任务的潜力.
一般目的的LLM与专业医疗LLM的表现相当.
ICF分类的复杂性可能需要比当前模型提供更深入的上下文理解.

结论:

使用LLM进行自动ICF分类是可行的,但需要仔细选择模型.
专门的医疗LLM在复杂的ICF编码中并非本质上优于一般的LLM.
需要进一步的研究来提高LLM的上下文理解,以提高医学分类的准确性.

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