从非小细胞肺癌临床笔记中提取治疗和反应,使用自然语言处理
Sonish Sivarajkumar1, Subhash Edupuganti2, David Lazris2,3
1Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA.
JCO clinical cancer informatics
|January 8, 2026
概括
这项研究开发了一个自然语言处理 (NLP) 系统,自动从临床笔记中提取癌症治疗和患者反应,改善非小细胞肺癌 (NSCLC) 的现实世界证据 (RWE) 生成. 该系统在将治疗方法与结果联系起来方面取得了很高的准确性.
科学领域:
- 计算瘤学是一种计算瘤学.
- 生物医学信息学是生物医学信息学.
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 从临床笔记中手动提取癌症治疗结果是耗时的,对现实世界的证据 (RWE) 产生具有挑战性.
- 自动化这一过程对于有效分析瘤学数据至关重要.
研究的目的:
- 开发和验证一个强大的NLP系统,自动从非小细胞肺癌 (NSCLC) 临床笔记中提取癌症治疗和基于RECIST的反应类别.
- 评估系统在将治疗与患者反应联系起来方面的表现.
主要方法:
- 一项回顾性研究使用250条注释的NSCLC瘤学笔记.
- 一个端到端的NLP管道,结合基于规则的实体提取和用于关系分类的机器学习模型 (生物医学临床BERT).
- 在持有测试集上的性能评估和部分外部验证.
主要成果:
- 该NLP系统的准确性很高,F1分数为0.92的关系分类和宏观平均F1分数为0.87的实体提取在UPMC测试集.
- 对于化疗和大多数反应类型,观察到高精度.
- 外部验证显示中等关系提取性能 (F1:0.51-0.64).
结论:
- 开发的NLP系统可靠地从非结构化的NSCLC瘤记录中提取结构化的治疗和反应信息,并具有高准确性.
- 这种自动化方法促进了关键癌症治疗结果的抽象,简化了瘤学中的RWE生成.
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