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人工智能辅助的自动心力衰竭检测和电子健康记录的分类
Mon Myat Oo1, Chuang Gao2, Christian Cole2,3
1Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, UK.
ESC heart failure
|May 3, 2024
概括
结合电子健康记录,深度学习和生物库,可以有效地识别心力衰竭 (HF) 亚型. 这种方法有助于自动检测和监测病例,以改善患者的护理和管理.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 生物标志物发现发现
背景情况:
- 与成像和生物数据集成的电子健康记录 (EHR) 提供了自动化患者护理改进的潜力.
- 深度学习 (DL) 算法可以分析复杂的医疗数据,包括心声图和生物标志物.
研究的目的:
- 通过使用电子心脏病记录数据,基于DL的回声心脏图分析和血生物标志物的组合,研究识别心力衰竭 (HF) 亚型的可行性.
- 开发和验证用于自动化HF患者队列选择的算法工作流.
主要方法:
- 使用了EHR数据 (1993-2021年) 与关键字搜索,ICD代码和处方数据.
- 应用DL用于对回声心脏图像DICOM图像进行自动解释.
- 血生物标志物的综合分析,将患者分为具有保留喷射分数 (HFpEF) 的HF,具有减少喷射分数 (HFrEF) 的HF和对照组.
主要成果:
- 一个算法工作流确定了578名患者 (186名对照,236名HFpEF,156名HFrEF) 的队列,来自6万多个EHR条目.
- 用DL增强的心声回声学提供了详细的心脏功能见解,揭示了各组之间显著的差异.
- 与对照组相比,HF患者的住院和死亡风险较高.
结论:
- 结合EHR关键字搜索,DL回声心电图解读和生物库资源,可以识别HF亚型.
- 这种综合方法在患者分类中表现出高度准确性,影响未来的HF诊断和管理.
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