自然语言处理自动提取食道炎的存在和严重程度在接受放射治疗的患者的笔记中
Shan Chen1,2, Marco Guevara1,2, Nicolas Ramirez1,2
1Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA.
JCO clinical cancer informatics
|July 28, 2023
概括
自然语言处理 (NLP) 模型在接受胸部放射治疗的患者中有效识别食道炎的严重程度. 这种自动化方法有助于从临床笔记中了解治疗毒性.
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:
- 放射治疗 (RT) 毒性显著影响患者的生存和生活质量,但往往研究不足.
- 临床笔记包含了关于治疗毒性的有价值的现实世界证据,但这些信息往往是无结构的,难以分析的.
- 需要自动化方法来从电子健康记录中提取和量化毒性数据.
研究的目的:
- 开发和评估自然语言处理 (NLP) 模型,以确定在接受胸部放射治疗的患者中食道炎的存在和严重程度.
- 根据临床笔记评估NLP模型在分类食道炎等级方面的表现.
- 研究这些NLP模型对不同患者群体的可转移性,例如食道癌患者.
主要方法:
- 使用了1524个手动注释的临床笔记 (黄金标准) 和2420个有结构化毒性数据的笔记 (银标准) 来自接受RT治疗的肺癌患者.
- 微调的统计和预训练的基于变压器的双向编码器表示 (BERT) 模型应用于三个食道炎分类任务.
- 模型的性能被评估在一个单独的345笔记,来自食道癌症患者接受RT.
主要成果:
- 微调的PubMedBERT实现了最高的性能,在三个分类任务中,宏观F1得分为0.92,0.82和0.74.
- 优化笔记部分选择至少提高了2%的性能,使用银标签数据至少提高了3%的性能.
- 这些模型表现出良好的可转移性,在没有进一步微调的情况下,在食道癌患者的笔记上达到0.73,0.74和0.65的宏F1得分.
结论:
- 本研究介绍了第一个自动化方法,用于从临床笔记中提取食道炎毒性严重程度,使用常用术语标准对不良事件 (CTCAE) 准则.
- 开发的NLP模型为扩大临床领域的自动化,详细的毒性监测提供了概念验证.
- 自动毒性提取可以显著提高对放射治疗相关不良事件的理解和管理.
相关概念视频
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