人工智能方法检测心力衰竭与保存的喷射分数在电子健康记录:一个公平的疾病检测模型
Jack Wu1, Dhruva Biswas1,2, Samuel Brown1,3
1School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London SE5 9NU, UK.
European heart journal. Digital health
|January 23, 2026
概括
一个新的模型,AIM-HFpEF,使用电子健康记录准确检测心脏衰竭与保存的喷射分数 (HFpEF). 这种公平的工具改善了诊断,特别是在代表性不足的人群中.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 保存喷射分数 (HFpEF) 的心力衰竭影响近一半的心力衰竭患者,造成显著的发病率和死亡率风险.
- HFpEF的低诊断是常见的,特别是在非白人人口中,传统的风险评分可能不那么准确.
- 现有的预测模型可能无法充分解决不同患者群体的诊断差异.
研究的目的:
- 根据欧洲心脏病学会 (ESC) 的标准,开发和验证HFpEF的公平诊断预测模型.
- 创建一个名为AIM-HFpEF的模型,能够使用电子健康记录 (EHR) 数据进行自动检测.
- 将AIM-HFpEF与H2FPEF和HFpEF-ABA等现有模型进行比较,以在不同人群中获得诊断准确性.
主要方法:
- 应用自然语言处理 (NLP) 和机器学习 (XGBoost) 对来自伦敦第三级医院信任的电子健康记录数据.
- 从3173名患者的人口,临床和心声回声图数据中推导出AIM-HFpEF模型.
- 在5383名患者身上对该模型进行了外部验证,并将其性能与H2FPEF和HFpEF-ABA进行了比较,特别是分析了非白人族群和社会经济贫困地区的表现.
主要成果:
- 在导出 (AUC=0.88) 和验证 (AUC=0.88) 队列中,AIM-HFpEF模型表现出强大的诊断性能.
- 在非白人族群的患者 (AUC=0.89) 和来自高社会经济贫困地区的患者 (AUC=0.90) 中观察到一致的诊断准确性.
- AIM-HFpEF的表现优于H2FPEF和HFpEF-ABA模型,并显示了与死亡,住院和中风风险增加相关的概率.
结论:
- AIM-HFpEF是HFpEF的验证,公平的诊断模型,可以集成到EHR系统中.
- 该模型促进完全自动化的HFpEF检测,解决诊断缺口.
- 这种方法有可能改善跨不同患者群体的HFpEF及时诊断和管理.
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