自然语言处理提高了COVID-19的可靠识别,而不是仅仅使用诊断码
Nathaniel Hendrix1, Rishi V Parikh2, Madeline Taskier1
1Center for Professionalism and Value in Health Care, American Board of Family Medicine.
American journal of epidemiology
|July 30, 2025
概括
对于COVID-19的诊断代码的准确性有限,特别是在老年人和黑人患者中. 自然语言处理 (NLP) 是有前途的,但需要经常更新,以可靠地识别患者队列.
科学领域:
- 医疗信息学 医疗信息学
- 医疗记录分析 医疗记录分析
- 流行病学研究 流行病学研究
背景情况:
- 关于COVID-19的观察性研究经常使用诊断代码来对患者进行分类.
- 这些代码的准确性和不同患者人口统计学中的差异性错误分类的可能性在很大程度上仍未得到审查.
- 了解这些局限性对于可靠的现实世界证据生成至关重要.
研究的目的:
- 评估用于识别COVID-19患者的诊断代码的准确性.
- 调查年龄,种族和种族作为差异错误分类的预测因素.
- 将诊断代码的性能与自然语言处理 (NLP) 分类器进行比较.
主要方法:
- 使用美国家庭队列的两个初级保健队列进行概念验证研究.
- 将ICD-10诊断代码与在医生评估的临床笔记上训练的NLP分类器进行比较.
- 三种NLP模型 (基于树的,循环神经网络,基于变压器) 被训练和测试.
主要成果:
- 只有63%的可能的COVID-19患者有记录的ICD-10代码.
- 老年患者 (75岁以上的60.6%) 和黑人患者 (58.5%) 的代码灵敏度较低.
- 一个基于树的NLP分类器实现了0.92的AUC,但在老年患者中显示精度下降,需要经常重新训练.
结论:
- 诊断代码在准确识别COVID-19患者方面存在重大局限性,年龄,种族和种族之间存在显著差异.
- NLP有可能改善队列识别,但需要持续的再培训来维持性能.
- 未来的研究应该专注于在观察性研究中准确分类患者的可靠方法.
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