基准测试变压器嵌入模型用于生物医学术语标准化标准化
Aditya Lahiri1, Sangeeta Shukla1, Ben Stear1
1The Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia PA, USA.
Machine learning with applications
|July 28, 2025
概括
大型语言模型 (LLM) 可以在临床试验注册表中标准化生物医学术语,提高数据的一致性. 这项研究将LLM与传统方法进行了比较,显示了术语标准化的卓越准确性.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 生物医学数据库的术语不一致,阻碍了机器学习和数据集成.
- 标准化术语对于有效使用生物医学数据至关重要.
研究的目的:
- 评估变压器/大语言模型 (LLM) 在NIH临床试验注册表 (CTR) 中标准化生物医学术语的有效性.
- 将基于LLM的方法与传统的文本匹配算法进行比较,使用世界卫生组织瘤分类 (WHO系统) 作为黄金标准.
主要方法:
- 开发了CANTOS (临床试验自动化命名和瘤本体标准化) 框架,从CTR中提取和标准化瘤名称.
- 与手动注释的世卫组织系统术语对比,对准了36种方法,包括LLM/变压器文本嵌入和传统算法.
- 使用1600个CTR瘤名称的样本评估准确性.
主要成果:
- 基于LLM/变压器的嵌入方法显著优于文本匹配方法,达到高达69.4%的准确性.
- 文本匹配方法的最大准确率为32.6%.
- 多数投票组合提高了准确度,达到71.9%.
结论:
- 在标准化生物医学术语方面,LLM/变压器嵌入模型是有效的.
- 坎托斯框架提供了一种可重复的方法,用于在生物医学数据标准化中对机器学习进行基准测试.
- 精确的术语标准化增强了生物医学数据库在研究和机器学习中的实用性.
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