在肝硬化急性损伤患者28天死亡率的基于机器学习的预测模型:MIMIC-IV数据库分析
Luyu Chai1, Yuxiang Zhou2, Nan Zhou3
1Hainan Lecheng Institute of Real World Study, Qionghai, Hainan Province, China.
PloS one
|September 8, 2025
概括
机器学习准确地预测了肝硬化急性损伤 (AKI) 患者的28天死亡率. 该模型强调了酸平衡指标,为这种高风险人群提供了改进的临床决策.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 肝病学 肝病学是一种肝病学.
- 数据科学在医学中的数据科学
背景情况:
- 肝硬化患者急性损伤 (AKI) 的死亡率很高.
- 对这一群体开发预测模型具有临床意义.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测肝硬化AKI患者28天死亡率.
- 确定这一群体中死亡率的关键预测因素.
主要方法:
- 从MIMIC-IV数据库对4,168名AKI患者 (601名肝硬化) 的回顾性分析.
- 数据预处理,特征选择 (递归特征消除) 和ML算法的评估.
- 对特征重要性和解释进行SHAP分析.
主要成果:
- 肝硬化组具有独特的特征 (年轻,较高的SOFA分数) 和显著较低的28天生存率.
- 一个随机森林模型实现了0.85的AUC和0.81.8的精确回忆面积.
- pH,离子间隙和总CO2被确定为关键预测因素,具有显著的相互作用.
结论:
- 一个经过验证的ML模型可以预测肝硬化AKI患者的28天死亡率.
- 该模型比传统的评分系统提供了更高的临床决策价值.
- 酸平衡指标对于评估这种患者群体的死亡风险至关重要.
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