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自然语言处理框架用于识别使用非结构化电子健康记录的腹腔大动脉动脉瘤修复

Daniel C Thompson^1,2, Reza Mofidi³

¹Vascular Surgery Specialty Training, Health Education England North East, Newcastle upon Tyne, UK. daniel.thompson28@nhs.net.

Scientific reports

|July 21, 2025

概括

自然语言处理 (NLP) 模型可以从电子健康记录 (EHR) 中准确识别和分类腹腔大动脉瘤 (AAA) 修复. 这种自动化改进了国家血管登记处的患者识别,减少了行政负担,并提高了数据捕获.

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.
健康数据科学数据科学

背景情况:

对于国家注册的患者鉴定通常依赖于手动方法或不准确的临床代码,导致数据缺口.
电子健康记录 (EHR) 包含非结构化的临床笔记,这些笔记难以手动分析.
需要自动化方法来提高医疗登记处患者识别的效率和准确性.

研究的目的:

开发和评估自然语言处理 (NLP) 模型,以识别和分类来自非结构化EHR的腹腔大动脉瘤 (AAA) 修复.
为了证明在血管外科注册表中使用NLP用于自动患者识别的可行性.
为了比较不同NLP模型在准确提取AAA维修信息方面的性能.

主要方法:

在MIMIC-IV-Note数据集上使用了多层NLP方法来识别血管患者,AAA修复,并对修复类型进行分类 (初级与修复).
训练和评估了四种NLP模型:scispaCy,BERT-base,Bio-clinicalBERT以及一个scispaCy/Bio-clinicalBERT组合.
用准确性,精度,回忆,F1得分和接收器操作特征曲线 (AUC) 下的面积来评估模型性能.

主要成果:

该scispaCy模型提供了最快的训练和推理时间.
高精度 (0.97) 通过scispaCy和组合模型来识别血管患者.
模型在识别AAA修复方面表现出色 (准确率为0.99,AUC为1.00),而Bio-clinicalBERT和整体模型在分类修复类型方面表现出色 (AUC为1.00,准确率为0.98).

结论:

NLP模型可以准确有效地从非结构化的EHR数据中识别和分类腹腔大动脉动脉瘤 (AAA) 修复病例.
使用NLP自动识别患者具有改善血管外科注册和其他医学数据收集工作的巨大潜力.
这种方法可以减少行政开销,提高注册表数据的完整性和准确性.

关键词:

一个AAA修复的修复.腹腔大动脉动脉瘤修复电子健康记录是电子健康记录.在NLP中,我们使用了NLP.国家血管注册局国家血管注册局自然语言处理自然语言处理.

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