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机器学习可以预测在接受血液透析的患者中透析后疲劳
Yuhan Zhang1, Jue Guo2, Na Yang1
1College of Nursing, Shanxi Medical University, Shanxi, China.
Renal failure
|July 27, 2025
概括
机器学习模型在中国血液透析 (HD) 患者中有效预测透析后疲劳 (PDF). 关键预测因素包括性,食欲和水平,有助于临床评估.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 透析后疲劳 (PDF) 是血液透析 (HD) 患者的普遍并发症.
- 准确的PDF预测模型对于改善患者护理至关重要.
- 机器学习 (ML) 为开发这种预测模型提供了潜力.
研究的目的:
- 探索各种ML模型在预测中国HD患者中PDF的有效性.
- 使用ML识别与PDF相关的关键临床因素.
主要方法:
- 一项涉及来自六家三级医院的1281名中国高发病患者的横截面研究.
- 七个ML模型 (LR,DT,RF,LGBM,CatBoost,XGB,GBT) 被评估用于PDF预测.
- 在报告研究结果时,遵循了TRIPOD+AI指南.
主要成果:
- 随机森林 (RF) 模型显示出最佳的性能 (AUC=0.855,精度=0.773).
- 显著的PDF预测因素包括性,食欲,水平,睡眠质量,便秘,囊手术史,腹压和并存疾病.
- 沙普利增量解释 (SHAP) 方法提供了模型可解释性.
结论:
- 机器学习模型提供了一个切实可行的工具,用于查和评估HD患者的PDF风险.
- 可解释的ML模型增强了对PDF决定因素的临床理解.
- 这一框架支持更好的临床决策管理PDF.
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