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将自然语言问题翻译成SNOMED表达式限制语言

¹The Australian E-Health Research Centre, CSIRO.

Studies in health technology and informatics

|November 14, 2025

概括

大型语言模型 (LLM) 现在可以将自然语言问题转换为SNOMED表达式约束语言 (ECL) 查询. 这一突破为非专家简化了临床概念查询,增强了数据分析和决策支持.

科学领域:

医疗信息学医疗信息学
人工智能的人工智能
临床术语学临床术语学

背景情况:

在临床决策支持和数据分析中,SNOMED表达式约束语言 (ECL) 是至关重要的.
ECL的复杂性和的学习曲线阻碍了非专业人士的采用.

研究的目的:

利用大型语言模型 (LLM) 开发一种新的方法,将自然语言问题翻译成SNOMED ECL.
通过简化ECL查询生成和解释,使SNOMED CT概念更容易获得.

主要方法:

利用最先进的LLM来实现自然语言和ECL之间的双向翻译.
开发定制数据集和一个新的管道,集成多个AI代理.
培训和评估基于LLM的模型,以提高ECL查询生成的准确性.

主要成果:

拟议的基于LLM的模型在将自然语言问题翻译成SNOMED ECL时实现了83.78%的准确性.
证明了模型对双向任务的能力:生成ECL并以自然语言解释ECL查询.

结论:

在弥合复杂的临床术语 (SNOMED ECL) 和自然语言之间的差距方面,LLM显示出显著的潜力.
这种方法可以通过提高SNOMED CT的可访问性来提高医疗保健的互操作性,临床决策支持和数据分析.
这项研究开创了人工智能用于SNOMED ECL翻译的应用,为更广泛的临床采用铺平了道路.

关键词:

这就是SNOMED CT.表达式约束语言 (ECL)大型语言模型 (LLM)自然语言处理 (NLP)

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