医学词汇的知识工程使用大型语言模型
Hsin Yi Chen1, Anna Ostropolets1,2, Chunhua Weng1
1Department of Biomedical Informatics, Columbia University, New York, NY, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
大型语言模型 (LLM) 显示了自动化医疗词汇任务的潜力,例如术语相似性和分组. 然而,目前的模型需要提高回忆和临床准确性,以全面管理医疗保健数据.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 医疗数据管理 医疗数据管理
背景情况:
- 医疗词汇对于医疗数据至关重要,但维护成本昂贵.
- 自动化词汇管理可以提高效率并降低成本.
研究的目的:
- 评估使用大型语言模型 (LLM) 来自动化医疗词汇管理的可行性.
- 评估LLM在术语相似性,附加和分组任务上的表现.
主要方法:
- 在来自SNOMED CT的1533个心血管条件上使用了GPT-4o.
- 对三个关键任务的OHDSI标准化词汇进行了LLM绩效的比较.
主要成果:
- 在术语相似性 (0.78),附属性 (0.74) 和分组 (0.78) 方面,LLMs实现了高精度.
- 召回率较低,特别是归纳 (0.08),表明覆盖率差距较大.
结论:
- 在医疗词汇任务的自动化方面,LLM表现有前途,但需要进一步改进.
- 未来的研究应该专注于改善回忆,减少错误和评估可扩展性.
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