OnSIDES数据库:使用自然语言处理模型从药物标签中提取不良药物事件
Yutaro Tanaka1, Hsin Yi Chen2, Pietro Belloni3
1Department of Biomedical Informatics, Columbia University Irving Medical Center, Columbia University, New York, NY 10032, USA; Department of Applied Physics and Applied Mathematics, Fu Foundation School of Engineering and Applied Sciences, Columbia University, New York, NY 10027, USA.
Med (New York, N.Y.)
|April 3, 2025
概括
一个新的数据库,OnSIDES,使用自然语言处理从药物标签中提取不良药物事件 (ADEs),创建一个机器可读的药物安全资源. 该资源有助于预测新药标,并了解跨药物类别的ADEs.
科学领域:
- 生物医学信息学 生物医学信息学
- 药物监督 药物监督 药物监督
- 自然语言处理自然语言处理.
背景情况:
- 药物不良事件 (ADEs) 是美国死亡和医疗保健成本增加的重要原因.
- 有限的机器可读数据库阻碍了系统的药物安全研究.
- 自然语言处理 (NLP) 的进步为从非结构化文本中提取药物安全信息提供了机会.
研究的目的:
- 开发一种机器可读的药物-药物不良事件 (ADE) 对的数据库.
- 利用NLP从结构化产品标签中准确提取ADEs.
- 创建一个全面的资源,以加强药物安全研究.
主要方法:
- 微调PubMedBERT模型以从FDA结构化产品标签中提取ADE术语.
- 编制药物-ADE对的OnSIDES (标签上的副作用资源) 数据库.
- 通过提取儿科,严重和国际ADEs来开发OnSIDES-INTL数据库.
主要成果:
- 在ADE提取方面取得了高性能,F1得分为0.90,AUROC为0.92,AUPR为0.95.
- OnSIDES数据库包含超过360万种药物-ADE对,用于来自47211个标签的3233种药物组合.
- 展示了潜在的应用,包括新药标预测和ADE丰富分析.
结论:
- 在SIDES作为一个有价值和全面的资源,推进药物安全研究.
- 该数据库有助于对药物-ADE关系进行系统分析.
- 能够预测新的药物标,适应症和ADEs.
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