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从临床检查笔记中提取医疗特征:两相大型语言模型框架的开发和评估

Manal Abumelha^1,2, Abdullah Al-Malaise Al-Ghamdi^1,3, Ayman Fayoumi¹

¹Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

JMIR medical informatics

|October 31, 2025

概括

本研究引入了一种新的两相框架,用于使用大型语言模型 (LLM) 提取医疗特征. 该框架显著提高了准确性,减少了幻觉和缺失的特征,即使训练数据有限.

科学领域:

自然语言处理自然语言处理.
医疗信息学医疗信息学
机器学习机器学习

背景情况:

从临床文本中提取医疗特征受到数据稀缺和术语变化的阻碍.
大型语言模型 (LLM) 是有前途的,但在医疗应用中与幻觉作斗争.

研究的目的:

为准确的医疗特征提取开发一个强大的LLM框架.
为了最大限度地减少幻觉和提高性能与有限的训练数据.

主要方法:

实施了两阶段的培训方法:指导微调和信心规范化微调.
该模型使用完整 (700 笔记) 和少量 (100 笔记) 数据集进行训练.
评估使用了USMLE Step-2临床技能数据集,并进行了广泛的测试.

主要成果:

实现了高F1得分 (0.968-0.983在完整的数据,0.960-0.973在少数射击数据),超过现有方法.
与基线LLM相比,幻觉减少了89.9%,缺失的特征减少了88.9%.
尽管性能有所改善,但表现出稳定的模型信心.

结论:

两个阶段的LLM框架提供了最先进的医疗特征提取与减少错误.
该框架表现出强大的概括性,在资源有限的设置中使用最小的数据表现良好.
该方法为自动化临床评估提供可靠的输出.

关键词:

自动化医疗评估临床NLP的临床NLP是什么减轻幻觉的发生减轻幻觉的发生指令调整调整的指令.大型语言模型.医学特征提取医学特征提取语义匹配是指语义上的匹配.

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