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风险模型的性能,以预测心力衰竭患者的死亡风险:综合卫生系统中的评估
Faraz S Ahmad1,2,3, Ted Ling Hu4, Eric D Adler5
1Division of Cardiology, Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, Suite 600, Chicago, IL, 60611, USA. faraz.ahmad@northwestern.edu.
概括
一个新的机器学习模型,MARKER-HF,有效地使用电子健康记录识别高风险心力衰竭 (HF) 患者. 这种工具比现有得分需要更少的数据准备,简化了专门护理的转诊.
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:
- 识别高死亡风险的心力衰竭 (HF) 患者对于及时的专家评估至关重要.
- 现有的风险预测工具在电子健康记录 (EHR) 系统中经常面临实施挑战.
研究的目的:
- 评估机器学习风险评估和心力衰竭 (MARKER-HF) 模型的EaRly死亡率的性能和实施方便性.
- 为了将MARKER-HF与已确定的风险得分进行比较,例如西雅图心力衰竭模型 (SHFM) 和慢性心力衰竭风险得分 (MAGGIC) 的全球基准分析组.
主要方法:
- 一项回顾性队列研究,涉及6764名患有HF的成年患者.
- 从2010年1月1日至2019年12月31日期间从电子健康记录中提取数据.
- 使用MARKER-HF,SHFM和MAGGIC估计了一年生存时间,通过AUC和图形校准评估歧视.
主要成果:
- 与SHFM和MAGGIC相比,MARKER-HF需要的数据工程和归算要少得多.
- 所有模型都表现出类似的歧视,AUC约为0.71.
- 标记器-HF,SHFM和MAGGIC在患者风险的整个频谱中显示出良好的校准.
结论:
- 马克尔-HF提供了一种更容易使用的方法,用于在门诊环境中识别高风险的HF患者.
- 该模型依赖于易于获得的EHR数据,这简化了它与临床工作流程的整合.
- 马克尔-HF促进了及时转诊到HF专家,可能改善患者的治疗结果.
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