对药理学视觉信息提取的多式人工智能进行评估
medRxiv : the preprint server for health sciences
|February 6, 2026
概括
从药理学文献中的视觉元素中以计算机辅助提取数据对于天然产品 (NP) 安全至关重要. 多式联运模型显示出前景,最好的模型准确地提取90%的表格数据和45%的图形数据.
科学领域:
- 药理学 药理学是指药理学的学科.
- 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:
- 越来越多地使用草药食补充剂 (天然产品) 引发了人们对处方药的相互作用的担忧.
- 天然产品的药监督需要对各种证据进行专家解释.
- 一个天然产品知识图 (NP-KG) 之前被显示为帮助安全调查.
研究的目的:
- 评估药理学文献中的视觉元素的计算机辅助数据提取的多式模式.
- 为了应对将NP-KG扩展到数千种天然产品的挑战.
- 提高自然产品安全调查的效率.
主要方法:
- 评估了8个开源和闭源多式联络模型.
- 从药理学文献中选择的表格和图像中进行视觉信息提取.
- 使用修改的相对错误率评估模型的准确性和弹性.
主要成果:
- 性能最好的模型在表格式数据提取方面实现了90%的准确性.
- 最好的模型从数字中提取了45%的数据.
- 较低的图像分辨率和更高的信息密度阻碍了提取性能.
结论:
- 多模式模型展示了从视觉药理学数据中计算机辅助数据提取的潜力.
- 需要进一步开发以克服与图像质量和复杂性相关的局限性.
- 精确的提取是扩展自然产品安全监督知识图的关键.
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