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通过机器学习和自然语言处理增强发生性心力衰竭的风险分层

Sirtaz Adatya¹, Anika S Naidu², Keane K Lee¹

¹Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, CA.

American heart journal

|September 24, 2025

概括

新的电子健康记录 (EHR) 模型准确地预测了心力衰竭 (HF) 住院和心力衰竭患者减少射出分数 (HFrEF) 的死亡. 这些模型改进了传统的基于声明的方法,以更好地管理患者.

科学领域:

心脏病学心脏病学
医疗信息学医疗信息学
预测分析是一种预测分析.

背景情况:

临床指导方针推了经过验证的心力衰竭风险模型,以减少喷射率 (HFrEF) 来指导预后和管理.
电子健康记录 (EHR) 为开发此类模型提供了丰富的数据来源.
以前的模型通常依赖于不太全面的索赔数据.

研究的目的:

开发和验证风险预测模型,以预测心力衰竭 (WHF) 恶化的住院情况和所有原因的死亡率在事件发生一年内.
将基于EHR的模型的性能与基于传统索赔的风险评分进行比较.

主要方法:

在综合医疗保健系统中,从2013年至2022年确定了发生HFrEF的成年人.
基于决策树的模型使用EHR数据开发,以预测WHF住院和死亡的1年风险.
模型性能使用交叉验证和保留测试组 (2021-2022),评估歧视 (AUC) 和校准 (Brier分数) 进行了评估.
使用验证的自然语言处理 (NLP) 算法识别了WHF住院病例.

主要成果:

在28292名HFrEF患者中,17.3%的WHF患者住院治疗,15.1%的患者在1年内死亡.
基于EHR的模型实现了WHF住院的AUC为0.698,死亡的AUC为0.849.
基于索赔的得分显示歧视较低 (WHF住院AUC为0.577).
在高风险患者中,很大一部分 (12.0%) 在诊断后6个月没有接受指导方针导向的治疗.

结论:

与基于索赔的方法相比,EHR衍生的风险模型在预测HFrEF患者1年的WHF住院和死亡率方面提供了更高的准确性.
这些验证的模型可以增强人口健康管理策略.
这些模型为HFrEF患者提供了有针对性的个性化护理干预措施.

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