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基于机器学习的中风预测在急诊室中的中风
Vida Abedi1,2, Debdipto Misra3, Durgesh Chaudhary4,5
1Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA, USA.
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概括
机器学习模型可以使用电子健康记录准确预测缺血性中风,改善急诊诊断. 这些模型表现出高性能,有助于开发关键中风预警系统.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床决策支持 临床决策支持
背景情况:
- 脑卒中误诊影响大约9%的中风患者,导致不良结果.
- 准确和及时诊断缺血性中风在急诊室 (ED) 中至关重要.
研究的目的:
- 调查机器学习 (ML) 模型在ED环境中诊断缺血性中风的有效性.
- 使用电子健康记录 (EHR) 开发和验证基于ML的预测模型.
主要方法:
- 利用了来自13家医院的56,452名患者接触的结构化和非结构化EHR数据 (2003年9月 - 2021年1月).
- 开发了结合自然语言处理 (NLP) 的ML管道,用于事件前的临床数据和提供者注释.
- 进行了前性时间验证,包括COVID-19前后期间,以及对误诊患者的案例研究.
主要成果:
- 使用事件前数据的ML模型实现了接收器操作特征曲线 (AUROC) 下的面积为0.88-0.92.
- 使用提供者笔记的模型表现出优异的性能,AUROC范围从0.93到0.99,灵敏度/特异性高达0.90和0.99.
- 在使用COVID-19前后数据进行训练的模型之间没有观察到显著的性能差异.
结论:
- 包括临床信息和提供者笔记在内的EHR数据对于构建强大的中风预测模型是有效的.
- 开发的ML模型可以作为先进ED中风预警系统的基础.
- 该研究强调了ML的潜力,以提高中风诊断准确性和患者的结果.
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