支持深度学习的自然语言处理用于识别定向的药理动力学药物相互作用
Joel Zirkle1, Xiaomei Han1, Rebecca Racz1
1Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, WO Bldg 64 Rm 2078, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
BMC bioinformatics
|November 2, 2023
概括
这项研究引入了一种新方法,可以从文本中自动识别药理学药物相互作用 (DDI) 的方向. 该模型在PK DDI句子中准确地区分了对象和沉剂药物.
科学领域:
- 生物医学信息学 生物医学信息学
- 药理动力学 药理动力学
- 自然语言处理自然语言处理.
背景情况:
- 在药物开发中,准确识别药理动力学 (PK) 药物相互作用 (DDI) 是至关重要的.
- 现有的自然语言处理 (NLP) 方法往往无法确定DDI的方向性 (对象与沉剂药物).
- 需要NLP工具,可以从生物医学文献和药物标签中具体提取PKDDI的方向.
研究的目的:
- 从文本中开发和评估一种自动识别PKDDI方向性的方法.
- 改善用于药物开发的临床相关DDI信息的提取.
- 为特定的DDI指导性任务解决NLP方法中的差距.
主要方法:
- 重新注释了文本分析会议 (TAC) DDI轨道2019年语料库,以包括DDI指导性.
- 在重新注释的语料库上微调了BioBERT模型.
- 通过TAC规定的培训和验证程序评估模型的性能.
主要成果:
- 该模型在识别含有PKDDI的句子时获得了0.82的F-score.
- 该模型在这些句子中正确识别对象与沉剂药物时获得了0.97的高F分数.
- 证明了使用微调的BioBERT用于精确的DDI方向性提取的可行性.
结论:
- 开发的方法在识别PK DDI方向性方面显示出高准确性.
- 强调需要在药物开发和信息学社区之间进行更好的协调,以创建特定任务的NLP集体和方法.
- 鼓励开发"适合目的"的NLP工具,以更好地支持药物开发过程.
相关概念视频
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