可解释的机器学习模型用于心脏手术后急性损伤的早期预测
Qing Sun1,2, Mengqing Ma3, Xia Du1
1Department of Nephrology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Aenue, Nanjing, 211100, Jiangsu, China.
International urology and nephrology
|October 13, 2025
概括
机器学习模型准确地预测心脏手术相关的急性损伤 (CSA-AKI). 使用这些模型早期识别高风险患者,可以及时进行干预,以改善结果.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學.
- 医疗信息学 医疗信息学
背景情况:
- 心脏手术相关的急性损伤 (CSA-AKI) 是一个显著的并发症.
- 开发CSA-AKI的准确预测模型对于患者管理至关重要.
研究的目的:
- 开发和验证用于预测心脏手术结束时CSA-AKI的机器学习 (ML) 模型.
- 使用数据驱动方法识别CSA-AKI的关键预测因素.
主要方法:
- 从2,277名心脏手术患者的临床数据回顾性收集.
- 使用拉索回归来识别11个关键预测因素的特征选择.
- 开发和评估六个ML算法,包括后勤回归,随机森林,GBDT,XGBoost,SVM和集合模型.
- 外部验证使用MIMIC-IV数据库 (1174名患者).
- 通过SHAP分析评估的模型解释性.
主要成果:
- 在初级队列中,CSA-AKI的发病率为22.6%.
- 术内红细胞 (RBC) 输血和心肺绕道 (CPB) 时间被确定为关键预测因素.
- 整体模型在内部验证中获得了最高的AUC (0.856).
- 外部验证证明了模型的稳定性,SVM表现最好 (AUC=0.847).
结论:
- 机器学习模型,特别是集体方法,可靠地预测CSA-AKI.
- 在不同患者队列中验证了可通用性.
- 这些模型可以早期识别高风险患者,以便及时进行术后干预.
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