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使用医学自然语言处理阐明塞莱科克西布在卡佩西塔诱导的手脚综合征中的预防作用.

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概括

自然语言处理 (NLP) 在电子健康记录中有效识别capecitabine诱导的手脚综合征 (HFS). 这种经过验证的NLP方法有助于对癌症患者进行现实世界的不良事件分析.

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科学领域:

  • 药物监督 药物监督 药物监督
  • 计算医学是一种计算医学.
  • 在瘤学瘤学.

背景情况:

  • 口服抗癌药物卡佩奇他 (Capecitabine) 通常会引起手脚综合征 (HFS),影响患者的生活质量和治疗坚持.
  • 在结构化电子健康记录 (EHR) 数据中检测像HFS这样的症状毒性是具有挑战性的.

研究的目的:

  • 验证一种自然语言处理 (NLP) 方法,用于从非结构化临床文本中识别capecitabine诱导的HFS.
  • 展示NLP在评估现实世界药物相关不良事件趋势中的应用.

主要方法:

  • 使用东京大学医院的电子健康记录数据 (2004-2021) 进行的回顾性队列研究.
  • 使用MedNERN-CR-JA NLP模型识别HFS病例.
  • 倾向性得分匹配,以比较带有和不带有赛莱科西布的capecitabine用户;Cox比例危险模型的时间到HFS发作.
  • 通过临床笔记的手动注释验证基于NLP的HFS检测.

主要成果:

  • 在669名capecitabine使用者中,HFS发病率明显高 (HR,1.93;P <.001).
  • 切莱科克西布的使用表明与降低HFS风险的相关性 (HR,0.51;P = .073).
  • 该NLP模型实现了高精度的HFS检测 (精度为0.875,回忆为1,000,F1得分为0.933).

结论:

  • 从现实世界的临床记录中检测HFS,NLP是有效的.
  • 这种NLP方法显示了回顾性药物安全性分析的潜力,正如 Celecoxib-HFS 调查所示.
  • 需要进一步的研究来评估这种NLP方法在不同临床环境中的通用性.