开发和验证可解释的机器学习模型,以预测血液透析患者的重症监护病房结果:一项多中心研究
Minjie Chen1,2, Pengan Li3, Yuanwen Xu1
1Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, No. 58, Zhongshan Road, Yuexiu District, Guangzhou, China.
BMC medical informatics and decision making
|December 3, 2025
概括
机器学习模型准确地预测血液透析患者的重症监护室 (ICU) 死亡率,优于传统的评分系统. 这使得高风险个体的早期识别和干预成为可能.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的机器学习
- 腎臟醫學 腎臟醫學
背景情况:
- 血液透析患者面临着 ICU 入院,死亡率,心血管疾病和感染的高风险.
- 传统的ICU评分系统 (例如,APACHE-II,SOFA) 对这个特定的患者群体的准确性有限.
- 早期识别高风险的血液透析患者对于及时干预和改善结果至关重要.
研究的目的:
- 确定血液透析患者ICU死亡率的关键风险因素.
- 开发和验证可解释的机器学习 (ML) 模型,用于预测ICU结果.
- 加强对这个弱势群体的早期干预策略.
主要方法:
- 这是一项多中心研究,利用了来自三个队列的数据:孙中山大学 (n=248),MIMIC-IV (n=769) 和eICU-CRD (n=1,878).
- 在ICU入院后24小时内,对113个临床变量应用13个ML算法和集合模型.
- 使用AUC进行性能评估,与现有的ICU评分系统进行比较,并对模型可解释性进行SHAP分析.
主要成果:
- 整体ML模型表现出高的预测准确性,优于传统的评分系统.
- 在所有死因中,NeuralNetC的AUC为0.847;在感染相关死亡中,ExtraTreesA的AUC为0.880;在心血管死亡中,NeuralNetD的AUC为0.790.
- 确定了死亡率的关键预测因素,并为临床应用开发了一个在线预测平台.
结论:
- 机器学习模型为血液透析患者的ICU死亡率提供了卓越的预测准确性.
- 这些模型有助于早期识别高风险个体,从而实现有针对性的干预.
- 开发的在线平台支持临床决策,并促进将研究成果转化为实践.
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