用机器学习预测脓毒症肾损伤患者重症死亡风险，准确率达87.8%。
Machine Learning-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database
- 基于MIMIC-IV和eICU数据库，筛选24个关键指标构建XGBoost模型。
- 内部验证AUROC达0.878，外部验证确保跨人群泛化性。
- 通过SHAP/LIME揭示乳酸、SOFA评分等核心风险因素，适合临床决策支持。
脓毒症相关急性肾损伤（SA-AKI）在重症监护中致死率高。本研究利用MIMIC-IV数据库对9,474例SA-AKI患者构建机器学习模型，预测住院死亡率，并在eICU协作研究数据库中进行外部验证。通过方差膨胀因子（VIF）、递归特征消除（RFE）及专家意见筛选出24个关键变量，采用极端梯度提升（XGBoost）模型并以网格搜索优化超参数。模型可解释性通过SHapley加性解释（SHAP）和局部可解释模型无关解释（LIME）增强。内部验证显示，该模型受试者工作特征曲线下面积（AUROC）为0.878（95%置信区间：0.859–0.897）。SHAP分析识别出序贯器官衰竭评估（SOFA）、血清乳酸和呼吸频率为关键预测因子；LIME则强调血清乳酸、急性生理与慢性健康评价II（APACHE II）评分、总尿量和血清钙的重要性。研究结果表明，结合先进算法与可解释性技术的XGBoost模型具备高准确性与可解释性，适用于多源人群的SA-AKI死亡风险预测，有助于早期识别高危患者，提升临床决策水平。未来需进一步提升模型适应性与实际应用能力。
原文摘要 · Abstract (English)
Background: Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to predict Intensive Care Unit (ICU) mortality in SA-AKI patients. External validation is conducted using the eICU Collaborative Research Database. Methods: For 9,474 identified SA-AKI patients in MIMIC-IV, key features like lab results, vital signs, and comorbidities were selected using Variance Inflation Factor (VIF), Recursive Feature Elimination (RFE), and expert input, narrowing to 24 predictive variables. An Extreme Gradient Boosting (XGBoost) model was built for in-hospital mortality prediction, with hyperparameters optimized using GridSearch. Model interpretability was enhanced with SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). External validation was conducted using the eICU database. Results: The proposed XGBoost model achieved an internal Area Under the Receiver Operating Characteristic curve (AUROC) of 0.878 (95% Confidence Interval: 0.859-0.897). SHAP identified Sequential Organ Failure Assessment (SOFA), serum lactate, and respiratory rate as key mortality predictors. LIME highlighted serum lactate, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, total urine output, and serum calcium as critical features. Conclusions: The integration of advanced techniques with the XGBoost algorithm yielded a highly accurate and interpretable model for predicting SA-AKI mortality across diverse populations. It supports early identification of high-risk patients, enhancing clinical decision-making in intensive care. Future work needs to focus on enhancing adaptability, versatility, and real-world applications.
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