在科学文章中识别数据集的生物医学实体:使用GPT-4o和PubTator 3.0的四步缓存增强生成方法
Claudia Giuliani1, Gita Benadi1, Felix Engel1
1Institute of Medical Biometry and Statistics, Medical Faculty and Medical Center, University of Freiburg, Stefan-Meier-Str. 26, Freiburg, 79104, Germany, 49 076127083739.
JMIR formative research
|November 20, 2025
概括
本研究引入了一种使用GPT-4o和PubTator 3.0进行自动化生物医学实体提取和数据集元数据注释的新的4步方法. 该方法实现了98%的注释精度,证明了其在常规生物医学元数据生成方面的潜力.
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
- 数据科学数据科学数据科学
背景情况:
- 准确提取生物医学实体对于研究数据集的有效元数据注释至关重要.
- 这确保了数据的可查,可访问性,互操作性和可重复使用性在协作研究中.
研究的目的:
- 为自动化生物医学实体识别引入一种新的四步缓存增强生成方法.
- 利用GPT-4o和PubTator 3.0进行数据集元数据注释.
主要方法:
- 一个四步过程:GPT-4o用于候选实体生成,PubTator 3.0用于验证,基于模式的术语提取和组合评估.
- 应用于来自OncoEscape协作研究中心的23篇文章,通过作者访谈和随机效应元分析进行验证.
主要成果:
- 每篇文章平均产生了19.6个与模式相关的生物医学实体和6.7个PubTator验证的生物医学实体.
- 总体注释精度达到了98% (95% CI 94%-100%),主要在非基础研究文章中出现错误.
- 包括补充材料并没有提高精度 (98%,95% CI 95%-100%).
结论:
- 大型语言模型显示了支持元数据注释工作流程的巨大潜力.
- 这些发现支持了用于常规生物医学元数据生成的全文分析的实际可行性.
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