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<p>Elderly patients with acute kidney injury (AKI) face a significantly increased mortality risk. Recent advances in machine learning technology have made it possible to predict the risk of death in patients at an early stage, which help to enable timely clinical intervention, optimize treatment strategies, and allocate hospital resources reasonably. We conducted a retrospective analysis of elderly patients admitted to the People’s Liberation Army General Hospital (PLAGH) between 2008 and 2018. This study included data on demographic characteristics, comorbidities, and laboratory test results. We employed five machine learning algorithms, including L2-regularized logistic regression (L2-logistic), Least Absolute Shrinkage and Selection Operator (LASSO), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Multi-layer Perceptron (MLP). To address the class imbalance issue , we employed oversampling techniques. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUC), and SHapley Additive exPlanations (SHAP) values were introduced to enhance the interpretability of the prediction models. A total of 1290 AKI patients were enrolled in the study, with a 28-day mortality rate of 25.43%. Through data oversampling, the XGBoost model with random oversampling was identified as the optimal predictive model. The model achieved an AUC of 0.8659 in the validation cohort. Furthermore, external validation was performed using the eICU Collaborative Research Database (eICU-CRD), yielding an AUC of 0.6317. SHAP analysis revealed that Mechanical Ventilation, Peak serum creatinine within 7 days, Stage of AKI, urine protein and sreum albumin levels were the top five predictive factors for 28-day mortality in elderly patients with AKI. This comprehensive approach demonstrates how predictive healthcare analytics can enhance clinical decision-making and ultimately improve patient outcomes.</p>

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Explainable machine learning-based 28-day mortality prediction model for elderly patients with acute kidney injury

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月5日

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  • Yueru Jiao,
  • Zhen Wu,
  • Yabin Zhang,
  • Yang Liu,
  • Peng Zhi,
  • Qiangguo Ao,
  • Qingli Cheng

摘要

Elderly patients with acute kidney injury (AKI) face a significantly increased mortality risk. Recent advances in machine learning technology have made it possible to predict the risk of death in patients at an early stage, which help to enable timely clinical intervention, optimize treatment strategies, and allocate hospital resources reasonably. We conducted a retrospective analysis of elderly patients admitted to the People’s Liberation Army General Hospital (PLAGH) between 2008 and 2018. This study included data on demographic characteristics, comorbidities, and laboratory test results. We employed five machine learning algorithms, including L2-regularized logistic regression (L2-logistic), Least Absolute Shrinkage and Selection Operator (LASSO), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Multi-layer Perceptron (MLP). To address the class imbalance issue , we employed oversampling techniques. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUC), and SHapley Additive exPlanations (SHAP) values were introduced to enhance the interpretability of the prediction models. A total of 1290 AKI patients were enrolled in the study, with a 28-day mortality rate of 25.43%. Through data oversampling, the XGBoost model with random oversampling was identified as the optimal predictive model. The model achieved an AUC of 0.8659 in the validation cohort. Furthermore, external validation was performed using the eICU Collaborative Research Database (eICU-CRD), yielding an AUC of 0.6317. SHAP analysis revealed that Mechanical Ventilation, Peak serum creatinine within 7 days, Stage of AKI, urine protein and sreum albumin levels were the top five predictive factors for 28-day mortality in elderly patients with AKI. This comprehensive approach demonstrates how predictive healthcare analytics can enhance clinical decision-making and ultimately improve patient outcomes.