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优化临床试验资格设计使用自然语言处理模型和真实世界的数据:算法开发和验证.

Kyeryoung Lee1, Zongzhi Liu1, Yun Mai1

  • 1GendDx (Sema4), Stamford, CT, United States.

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此摘要是机器生成的。

自然语言处理 (NLP) 通过创建符合条件的知识库来简化临床试验. 这种数据驱动的方法增强了患者识别,并优化了试验设计,以更快地开发药物.

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临床试验资格标准 临床试验资格标准临床试验协议优化 临床试验协议优化数据驱动的方法数据驱动的方法.资格标准 具体的本体论 具体的本体论自然语言处理自然语言处理.现实世界的数据.

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科学领域:

  • 计算语言学计算语言学
  • 生物医学信息学是生物医学信息学.
  • 临床试验的管理管理.

背景情况:

  • 临床试验对于新疗法至关重要,但往往面临延迟.
  • 高效的数据管理,协议优化和患者识别是减少试验时间表的关键.
  • 自然语言处理 (NLP) 为这些挑战提供了潜在的解决方案.

研究的目的:

  • 评估数据驱动的方法,以优化临床试验协议和识别符合条件的患者.
  • 开发一个全面的资格标准知识库,与电子健康记录集成,使用基于深度学习的NLP.

主要方法:

  • 从3281个临床试验 (2013-2020年) 中提取了符合条件的标准,使用定制的NLP管道 (双向LSTM-CRF).
  • 将hypernym概念转换为可计算的hyponyms,用于知识库.
  • 利用2775名非小细胞肺癌患者的一个子集进行试点模拟.

主要成果:

  • 手动注释了14.78%的试验,创建了一个资格标准本体学.
  • 通过NLP管道实现了高精度 (0.91),回忆 (0.79) 和F1得分 (0.83).
  • 开发了一个标准化,EHR兼容的知识库和一个试验优化和患者识别的原型接口.

结论:

  • 在NLP管道成功创建了一个标准化,机器可读的资格标准知识库.
  • 原型接口展示了使用现实世界的数据来评估标准对患者资格的影响的可行性.
  • 整合NLP和现实世界的数据提供了一个有希望的方法来简化临床试验和提高患者识别效率.