从使用语言模型的非结构化瘤学笔记中提取和计算东部合作性瘤学小组的绩效状况
Wenxin Xu1,2, Bowen Gu1, William E Lotter1,2
1Dana-Farber Cancer Institute, Boston, MA.
JCO clinical cancer informatics
|May 29, 2024
概括
自然语言处理 (NLP) 模型可以从非结构化的临床笔记中准确地计算东部合作性瘤组 (ECOG) 的性能状态 (PS). 这种方法提高了电子健康记录的可用性,用于癌症研究和患者护理.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 东部合作瘤学集团 (ECOG) 的绩效状态 (PS) 对癌症治疗和研究至关重要.
- ECOG PS通常以电子健康记录中的非结构化文本形式记录,限制了其直接使用.
- 需要自动化方法来提取和利用这些有价值的临床数据.
研究的目的:
- 调查自然语言处理 (NLP) 模型在从非结构化瘤学家笔记中归因ECOGPS的有效性.
- 评估NLP归算的ECOGPS与整体存活率 (OS) 之间的关联.
主要方法:
- 利用了来自79,698名患者 (1997-2023) 的495,862份医疗瘤学笔记的数据集.
- 提取的正则表达式明确记录了培训数据的PS.
- 训练了一个基于变压器的长变压器模型,从删除了明确PS文档的笔记中计算ECOG PS (0-1对2-4) .
主要成果:
- 长型模型在赋值 ECOG PS (测试组 AUC 0.95) 中实现了高分辨率.
- 假定的表现不佳状态与整体存活率较差 (HR 11.9) 有显著的关联.
- 这种关联甚至在缺乏明确PS文档的笔记中仍然明显.
结论:
- NLP模型提供了一个可扩展的解决方案,用于从非结构化的瘤学家笔记中归因性能状态.
- 这种方法可以显著帮助对临床结果研究的瘤学数据集进行注释.
- 以NLP驱动的归算可以通过利用以前无法访问的数据来改善癌症护理的提供.
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