从生物医学文献中提取预分析变量数据的AI:可行性和验证
Vicky Scholz1, Sven Bichtemann1, Oliver Johannes Bott2
1Hannover Unified Biobank (HUB), Medizinische Hochschule Hannover, Carl Neuberg Str.1, 30625 Hannover.
Studies in health technology and informatics
|September 3, 2025
概括
大型语言模型 (LLM) 显示出从科学文献中提取预分析变异性数据的前景. 有针对性的改进提高了提取质量,但对复杂的生物医学数据仍然需要专家监督.
科学领域:
- 生物医学研究
- 数据科学
- 科学文献分析
背景情况:
- 预分析变异性对生物样本研究的质量和可重复性产生重大影响.
- 预分析条件的标准化报告是有限的,阻碍了系统的评估.
- 这项研究研究了用于结构化数据提取的大型语言模型 (LLM).
研究的目的:
- 探索LLM用于提取分析前变异性数据的潜力.
- 评估LLM在理解和结构复杂的实验条件上的表现.
- 评估LLM在生物医学数据提取方面的有效性.
主要方法:
- 使用标准化参数目录进行LLM评估.
- 使用专门设计的提示来测试各种LLM.
- 基于上下文理解和结构化输出生成的绩效评估.
主要成果:
- 一些LLM (例如GPT-4.5,DeepSeek R1) 在上下文理解和CSV输出方面表现出色.
- 对存储时间和温度等复杂条件进行一致的语义映射带来了挑战.
- 发现有针对性的令牌减少显著提高了开采质量.
结论:
- 在生物医学研究中,LLM是支持结构化数据提取的有效工具.
- 目前LLM在可复制性和上下文忠实性方面的局限性需要专家监督.
- 需要进一步开发以应对复杂实验参数的挑战.
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