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机器学习模型用于预测4期慢性病患者的短期进展:一个多中心验证研究

Jingshu Li^1,2,3, Xuanyi Du⁴, Rui Zhang¹

¹Hemodialysis Center, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

Scientific reports

|November 10, 2025

概括

机器学习模型可以预测第四阶段慢性病 (CKD) 患者的短期进展到末期病 (ESRD). XGBoost模型表现出强的表现,识别了高风险患者,以便更好地规划护理.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
医疗信息学医疗信息学
机器学习机器学习

背景情况:

末期病 (ESRD) 具有显著的发病率和死亡率风险.
预测第四阶段慢性病 (CKD) 的短期进展对于患者管理至关重要.
早期识别高风险患者可以提高先进的护理规划和改善结果.

研究的目的:

开发和验证一种机器学习 (ML) 模型,用于预测第四阶段CKD患者在25周内进展到ESRD.
评估各种ML模型的性能,包括XGBoost,用于ESRD风险预测.
确定预测短期ESRD进展的关键临床特征.

主要方法:

对451名患有第四阶段CKD的患者的电子健康记录 (EHR) 的分析.
9个ML模型的开发和比较,包括Ridge,Random Forest和XGBoost.
使用不同的患者队列对表现最佳的模型进行内部和外部验证.

主要成果:

在内部验证中,XGBoost模型获得了最高的性能,AUC为0.93,F1得分为0.89.
外部验证显示了强大的概括性,XGBoost的AUC为0.85,F1得分为0.79.
关键的预测特征包括高密度脂蛋白胆固醇,白蛋白,囊素C,阿波蛋白B,纤维素B,血尿素和中性粒细胞数量.

结论:

机器学习模型可以有效地利用可访问的临床数据预测第四阶段CKD患者的短期ESRD进展.
XGBoost模型显示出卓越的性能和临床查的潜力,在快速ESRD进展的高风险患者.
准确的预测有助于及时进行干预,并改善对CKD进展的患者管理策略.

关键词:

最终阶段的脏疾病.机器学习是机器学习.预后预后预测预测风险预测模型的风险预测模型.短期进展短期进展第四个阶段CKD

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