支持药物监测的NLP:质量不良药物反应活性控制 (四位数)
Andrea Franchini1, Roberta Noseda2, Joseph Cornelius1
1Dalle Molle Institute for Artificial Intelligence Research, IDSIA USI-SUPSI, Polo Universitario, Lugano, Switzerland.
Clinical pharmacology and therapeutics
|February 27, 2026
概括
自然语言处理 (NLP) 可以在电子健康记录中识别药物不良反应 (ADR),改善药物监督. 与传统方法相比,NLP系统检测到ADR的数量是传统方法的两倍,提高了患者的安全性.
科学领域:
- 药监和自然语言处理 (NLP)
背景情况:
- 药物不良反应 (ADRs) 显著导致发病率和死亡率.
- 由于数据的限制,当前的药物监测方法报告的副作用不足.
- 电子健康记录 (EHR) 为ADR检测提供了丰富的临床叙述来源.
研究的目的:
- 开发和评估NLP系统,用于检测和提取EHR排放摘要中的ADR信息.
- 将机器学习和深度学习方法与ADR识别的传统方法进行比较.
主要方法:
- 追溯的横截面研究,使用400个排放摘要用于ADR分类模型培训.
- 机器学习 (逻辑回归与词袋) 和regex系统的比较.
- 使用两步深度学习管道对药物和临床事件提取的评估与基于字典的方法对100个注释总结.
主要成果:
- 使用词袋的逻辑回归在ADR分类和案例排名中表现出卓越的表现.
- 在模拟部署中,NLP模型发现了与regex系统相比,ADR含有摘要的数量几乎翻了一番.
- 深度学习管道显著优于药物和事件提取和分类的基于字典的方法.
结论:
- 从EHR中对临床叙述的NLP分析可以大大提高药监工作.
- 开发的NLP系统提高了ADR检测的准确性和效率.
- 这些方法支持药物监测,同时有效地管理专家审查工作量.
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