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开发和评估SNOMED CT自动映射工具:推进术语标准化和语义互操作性
Youngsun Park1, Hannah Kang1, Jiwon Kim1
1Kakao Healthcare Corp, Seongnam-si, Republic of Korea.
JMIR medical informatics
|March 9, 2026
概括
本研究引入了一种使用大语言模型 (LLM) 的自动化工具,以简化SNOMED CT (医学临床术语系统化命名法) 映射和概念创作,显著提高医疗数据标准化的效率和准确性.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 健康 数据科学 数据科学
背景情况:
- 医疗保健数据的碎片化阻碍了由于非标准化术语的二次使用.
- 手动的SNOMED CT (医学临床术语系统化命名法) 映射是劳动密集型和不一致的.
- 自动化解决方案对于可扩展的临床术语标准化至关重要.
研究的目的:
- 开发一个大型语言模型 (LLM) 辅助工具,用于高效的 SNOMED CT 术语映射和概念创作.
- 实现跨多机构临床数据集的无,标准化的数据整合.
- 改善医疗保健数据中的语义互操作性.
主要方法:
- 一个管道涉及本地术语预处理,基于LLM的矢量相似度映射和代丰富.
- 使用GPT-4o进行翻译和语义表示.
- 针对新概念创作的结构化后协调,并对效率和质量进行定量评估.
主要成果:
- 在诊断 (高达98.7%) 和外科手术程序 (高达99.2%) 绘制方面取得了高的top-5准确性.
- 减少了30%的手动绘图率和高达90%的整体工作量.
- 缩短了约75%的映射/创建时间和83%和72%的错误 (重复,违反规则).
结论:
- 一个自动化的,LLM辅助的SNOMED CT映射工具显著提高了效率,准确性和概念质量.
- 未来的工作包括利用本体结构,知识图和自动规则执行来实现强大的标准化.
- 解决技术整合和翻译质量问题是更广泛采用的关键限制.
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