从生物医学文献中提取临床药物相互作用的超对比学习方法
Yaxun Jia1, Zhu Yuan2, Lian Zhu1
1Department of Radiation Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
PLoS computational biology
|December 5, 2025
概括
本研究介绍了BioMCL-DDI,这是一个用于从生物医学文本中提取药物相互作用 (DDI) 的几次射击学习框架. 它在低资源环境中实现了高精度,提高了患者的安全性.
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.
- 药物监督 药物监督 药物监督
背景情况:
- 药物相互作用 (DDI) 是药物不良事件的主要原因,对患者的安全构成风险.
- 从文献中提取DDI对于药物监测至关重要,但由于数据限制,这具有挑战性.
- 现有的方法在DDI提取中扎着数据稀疏性和高注释成本.
研究的目的:
- 为从生物医学文献中提取DDI开发一个高效的短暂学习框架.
- 为应对数据稀疏性和DDI识别有限监督的挑战.
- 提高DDI提取的准确性和可扩展性,以提高患者的安全性.
主要方法:
- 开发了BioMCL-DDI,这是一个新的框架,将元学习与对比嵌入策略集成在一起.
- 采用统一的架构来共同优化基于原型的分类和对比的表示学习.
- 确保了类内紧性和类间可分离性,以便在稀疏的数据设置中进行更好的概括.
主要成果:
- 获得了高的F1分数:在DDI-2013上达到了87.80%,在DrugBank上达到了86.00%,在TAC 2018上达到了74.85%/74.82%.
- 持续优于竞争基线的表现,特别是在低资源场景中.
- 在有限的监督环境中,与最先进的方法相比,表现出优越的性能.
结论:
- 生物MCL-DDI提供了一个可扩展和有效的解决方案,用于从生物医学文本中提取药物相互作用.
- 该框架显示了集成到临床决策支持系统和知识库中的强大潜力.
- 公开发布的代码和数据有助于进一步的药监研究和应用.
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