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在临床自由文本摘要中使用大型语言模型支持识别智力障碍:混合方法研究研究

Aleksandra Edwards¹, Antonio F Pardiñas², George Kirov²

¹School of Computer Science and Informatics, Cardiff University, Cathays, Cardiff, CF24 4AG, United Kingdom, 1 029 2087 4812.

|September 18, 2025

概括

大型语言模型 (LLM) 可以有效地从临床笔记中识别智力障碍 (ID) 的患者,使用少数射击学习. 这种方法有助于识别临床意义的患者亚组,并得到遗传验证的支持.

科学领域:

人工智能在医学中的应用
临床信息学临床信息学
遗传学遗传学是一个

背景情况:

自由文本临床数据提供了丰富的患者信息,但对研究质量的表型提取提出了挑战.
手动提取是耗时的,不适合大数据集.
由于缺乏注释资源,自动化方法受到阻碍.

研究的目的:

开发一个大型的语言模型 (LLM) 管道,使用一些射击学习来从自由文本摘要中提取临床信息.
评估管道在严重精神疾病患者共患智力障碍 (ID) 的分类方面的表现.
为了对LLM识别的ID病例进行遗传验证.

主要方法:

开发了一种两阶段的分类方法,结合了信息提取 (IE) 和人类循环技术.
评估了微调语言文本到文本传输变压器 (Flan-T5) 和大型语言模型架构 (LLaMA) 模型.
用了1144份临床摘要 (314份注释) 和来自547名个体的遗传数据进行验证.

主要成果:

通过手动验证的两步方法,有效地从自由文本记录中识别了可疑的ID,使用最小的培训数据.
带有IE的Flan-T5型号获得了F1得分0.867.7,即0.867.
通过LLM识别的ID病例显示,发育障碍风险基因的de novo变异具有显著的丰富性 (OR 29.1,P=2.1×10-5).

结论:

临床知识博,上下文学习和循环中的人类方法增强了临床数据的提取和分类.
LLM可以识别患有严重精神疾病和疑似ID的患者,这是一个有意义的子组.
这项概念验证研究证明了LLM在临床应用中的实用性.

关键词:

临床注意事项临床注意事项基因分析遗传分析提取信息提取信息智力障碍智力障碍是一种智力障碍.大型语言模型.文字分类文本分类文本分类零射击学习的学习

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