用机器学习提前预测老年手术患者术后中风风险，关键指标可解释。
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
- 基于前24小时临床数据，结合特征筛选与可解释模型预测中风
- 最佳模型AUROC达0.887，识别出既往脑血管病等三大关键风险因素
- 适合临床医生用于术前风险评估，模型结果透明可追溯
术后中风是老年手术重症监护病房（SICU）患者的重要并发症，导致住院时间延长、医疗费用增加和死亡率上升。准确的早期风险分层对及时干预和改善预后至关重要。本研究整合MIMIC-III和MIMIC-IV数据库中的19,085例老年SICU入院数据，构建了一个可解释的机器学习框架，利用患者入住ICU首24小时内的临床数据预测住院期间中风发生。预处理包括剔除高缺失变量、迭代奇异值分解（SVD）插补、z-score标准化、独热编码及通过自适应合成采样（ADASYN）算法校正类别不平衡。采用两阶段特征选择：递归特征消除结合交叉验证（RFECV）与SHapley加性解释（SHAP），将初始80个变量缩减至20个临床相关预测因子。在比较的八种机器学习模型中，CatBoost表现最优，曲线下面积（AUROC）为0.8868（95%置信区间：0.8802–0.8937）。SHAP分析与消融实验表明，既往脑血管疾病、血清肌酐和收缩压是最具影响力的风险因素。结果表明，可解释的机器学习方法在支持术后中风早期检测和围术期重症监护决策方面具有潜力。
原文摘要 · Abstract (English)
Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs, and increased mortality. Accurate early risk stratification is essential to enable timely intervention and improve clinical outcomes. We constructed a combined cohort of 19,085 elderly SICU admissions from the MIMIC-III and MIMIC-IV databases and developed an interpretable machine learning (ML) framework to predict in-hospital stroke using clinical data from the first 24 hours of Intensive Care Unit (ICU) stay. The preprocessing pipeline included removal of high-missingness features, iterative Singular Value Decomposition (SVD) imputation, z-score normalization, one-hot encoding, and class imbalance correction via the Adaptive Synthetic Sampling (ADASYN) algorithm. A two-stage feature selection process-combining Recursive Feature Elimination with Cross-Validation (RFECV) and SHapley Additive exPlanations (SHAP)-reduced the initial 80 variables to 20 clinically informative predictors. Among eight ML models evaluated, CatBoost achieved the best performance with an AUROC of 0.8868 (95% CI: 0.8802--0.8937). SHAP analysis and ablation studies identified prior cerebrovascular disease, serum creatinine, and systolic blood pressure as the most influential risk factors. Our results highlight the potential of interpretable ML approaches to support early detection of postoperative stroke and inform decision-making in perioperative critical care.
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