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PheCatcher:利用LLM生成的合成数据从生物医学文献中自动提取表型定义

Yan Hu¹, Na Hong², Yiming Li¹

¹McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Studies in health technology and informatics

|August 8, 2025

概括

本研究介绍了PheCatcher,这是一个自动化管道,用于从生物医学文献中提取表型定义. 利用GPT-4生成的合成数据显著提高了表型信息提取的准确性.

科学领域:

生物医学信息学生物医学信息学
计算生物学计算生物学
自然语言处理自然语言处理.

背景情况:

现型定义对于提高精度和个性化医学的发展至关重要.
现有的表型知识库通常需要大量的手动输入,限制了可扩展性.
需要自动化方法来有效地从生物医学文献中提取和标准化表型信息.

研究的目的:

开发一个自动化的管道,PheCatcher,从生物医学文献中提取表型定义和标准化代码.
评估由GPT-4生成的合成数据对提高信息提取模型性能的影响.
创建一个公开可访问的提取的表型定义的存储库.

主要方法:

PheCatcher集成了基于BiomedBERT的命名实体识别 (NER) 和关系提取 (RE) 来进行表型识别.
使用GPT-4生成合成数据来增强NER和RE模型的训练数据集.
该管道适用于PubMed Central (PMC) 文章的大型机构.

主要成果:

对于表型实体,NER模型的F1得分从0.616提高到0.800,使用合成数据.
该RE型号实现了高F1得分0.901.1,达到0.901.
该管道成功地从PMC文章中提取了173,283个表型定义.

结论:

像PheCatcher这样的自动信息提取管道可以显著增强表型知识库的创建.
使用大型语言模型 (例如,GPT-4) 生成合成数据是改善IE系统性能的一种可行和有效的策略.
开发的系统为表型数据提供了一个有价值的,公开可访问的资源,支持精准医学研究.

关键词:

提取信息提取信息现象型定义定义定义综合数据综合数据

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