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高通量生物医学关系提取半结构化网页文章的大型语言模型赋权.

Songchi Zhou1, Sheng Yu2

  • 1Department of Statistics and Data Science, Tsinghua University, Beijing, China.

BMC medical informatics and decision making
|September 30, 2025
PubMed
概括

域调整的大型语言模型 (LLM) 在从半结构化网站中提取生物医学关系方面表现出卓越的性能. 这些模型为高通量生物医学知识提取提供了可扩展的解决方案,有利于临床应用.

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:

  • 为半结构化网站开发高通量生物医学关系提取系统.
  • 利用大语言模型 (LLM) 与阅读理解和医学知识.

研究的目的:

  • 创建一个专门的系统,从半结构化的网络内容中提取生物医学关系.
  • 评估各种LLM的有效性,包括通用,适应领域和参数有效的模型.

主要方法:

  • 关系提取是用LLMs制定的二进制分类任务.
  • 简单的LLM提供了提取关系的事实验证的理由.
  • 通过生物医学词典匹配识别的候选实体;主要标题作为尾部实体.

主要成果:

  • 适应领域的LLM显著优于通用模式.
  • MedGemma-27B (F1=0.820) 超过了GPT-4o和GPT-4.1;DeepSeek-V3获得了最好的表现 (F1=0.844).
  • 从权威的生物医学网站提取了超过225,000个关系三胞胎,跨越三个关系类型.

结论:

  • 在高通量生物医学关系提取方面,LLM是有效的,其适应领域的模型提供了实际优势.
关键词:
人工智能的人工智能是人工智能.生物医学关系提取提取大型语言模型.

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  • 该框架具有可扩展性和适应性,可用于跨异构网站的多种生物医学关系.
  • 提取的关系可以增强知识图,支持基于证据的指导方针,并帮助临床决策.