使用自然语言处理识别和验证呼吸道病毒免疫.
Kevin A Wilson1, John J Riddles1, Andrew C Hill2
1Westat, Bethesda, MD, United States.
Frontiers in digital health
|February 18, 2026
概括
这项研究开发了一种自然语言处理 (NLP) 算法,用于识别电子健康记录 (EHR) 中的免疫. 该NLP工具显示了COVID-19和流感疫苗的高准确性,增强了EHR数据.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 公共卫生 公共卫生
背景情况:
- 电子健康记录 (EHR) 通常在非结构化的文本注释中包含有价值的免疫数据.
- 结构化EHR数据可能并不总能捕捉到免疫接种的全部范围.
研究的目的:
- 开发和评估基于规则的自然语言处理 (NLP) 算法,用于在EHR叙述中识别COVID-19,流感和RSV免疫.
- 评估NLP算法的性能与手动审查和结构化的电子健康记录数据相比.
主要方法:
- 一个多阶段的,基于规则的NLP算法是使用通用和特定的关键字,考虑语法依赖,开发的.
- 该算法包含了否定和免疫推的规则.
- 在20,000名患者的样本上,使用精度和回忆指标评估了表现,并对400条笔记进行了手动审查.
主要成果:
- 手动审查显示COVID-19 (71%,97%) 和流感 (91%,92%) 免疫接种的高精度和回忆.
- 在手册审查过程中,RSV免疫识别实现了57%的精度和96%的召回.
- 与结构化数据进行比较,所有疫苗类型的召回率较低 (9-12%),但准确度可接受 (72-78%).
结论:
- NLP算法有效地识别了EHR文本中提到的免疫,特别是COVID-19和流感.
- 与结构化数据相比,低回忆率表明仅从文本中捕获所有免疫的局限性.
- 该算法为增强免疫记录提供了专门的实用程序,并作为未来EHR研究的框架.
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