开发一种可解释的机器学习模型,用于预测老年高血压患者的4年慢性病风险
Panji Wang1, Yuan Meng1, Zhaowei Sun1
1Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
International journal of medical informatics
|February 4, 2026
概括
我们开发了一种可解释的机器学习模型,用于预测患有高血压的老年人慢性病 (CKD) 风险. 该工具使用常规的临床指标,适用于各种人群.
科学领域:
- 老年学是一门学科.
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗保健中的机器学习
背景情况:
- 年龄和高血压是导致老年人功能下降和死亡率增加的重要危险因素.
- 预测慢性病 (CKD) 风险对于在这个脆弱人群中及时干预至关重要.
研究的目的:
- 开发一种可解释的机器学习模型,用于预测4年的CKD风险.
- 确定老年高血压患者中CKD的关键预测因子.
- 为医疗保健专业人员创建一个临床适用的预测工具.
主要方法:
- 在健康和退休研究 (HRS) 中利用了4142名高血压患者的数据进行开发和验证.
- 采用LASSO-Boruta算法进行特征选择,确定了九个常规临床预测因素.
- 构建和评估了八个机器学习模型,其中XGBoost通过SHAP分析选择了其性能和可解释性.
主要成果:
- 在时间验证 (AUC=0.702) 中,XGBoost表现出卓越的性能,并且提供了良好的灵敏度和特异性的平衡.
- SHAP分析确定年龄是CKD风险的主要预测因素.
- 开发的模型显示,数据集中预测和观察到的风险之间存在稳定的一致性.
结论:
- 成功开发了一种可解释的机器学习模型,用于预测老年高血压成年人的4年CKD风险.
- 该模型利用了常规的临床指标,并证明了亚洲和高加索人群的适用性.
- 这种工具可以帮助在风险人群中早期发现和管理CKD.
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