可解释的机器学习模型用于预测慢性重症和心力衰竭患者的住院死亡率:一项多中心研究
Min He1, Yongqi Lin1, Siyu Ren1
1Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Digital health
|June 9, 2025
概括
机器学习准确地预测了慢性严重疾病和心力衰竭患者的医院死亡. 这种工具有助于早期干预,以改善重症监护病房患者的治疗结果.
科学领域:
- 关键护理医学 关键护理医学
- 生物医学信息学 生物医学信息学
- 心脏病学 心脏病学
背景情况:
- 心力衰竭 (HF) 是重症监护室 (ICU) 中死亡的重要原因,特别是在慢性重症病 (CCI) 患者中.
- 预测这种脆弱人群的住院死亡率对于及时干预至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测CCI和HF患者的住院死亡率.
- 确定这一患者群体中死亡率的关键预测因素.
主要方法:
- 利用了来自MIMIC-III,MIMIC-IV和eICU-CRD数据库的回顾性数据,包括780名衍生队列中的患者和610名验证队列中的患者.
- 采用递归特征消除来选择11个关键预测变量.
- 评估了多个ML算法,包括支持向量机 (SVM),随机森林和K-最近的邻居,通过接收器操作特征曲线 (AUROC) 下的区域评估性能.
- 应用了SHapley添加式解释 (SHAP) 来实现模型的可解释性.
主要成果:
- 在SVM模型中,导出队列的AUROC为0.781,验证队列的AUROC为0.675.
- 通过SHAP分析确定的关键预测因素包括顺序器官衰竭评估 (SOFA) 评分,氧血球和和血压.
- 该模型证明了医院死亡率的强大预测准确性.
结论:
- 开发的SVM模型可靠地预测了CCI和HF患者的住院死亡率.
- 这种ML工具可以支持早期干预策略,并改善重症监护机构的患者管理.
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