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<p>We aimed to construct and validate interpretable models for predicting mortality risk using machine learning (ML) methods to identify the risk factors associated with mortality in patients with diabetic neuropathy (DN). We selected patients from the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-IV)). Independent risk factors for in-hospital death were screened using Least Absolute Shrinkage and Selection Operator (LASSO). Subsequently, we constructed mortality risk prediction models utilizing random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and logistic regression (LR). Finally, we comprehensively assessed and interpreted the best-performing ML model using SHapley Additive exPlanations (SHAP) analysis. The final study enrolled 1,313 patients with DN, randomly split into training (1,050, 80%) and validation sets (263, 20%). The RF model demonstrated superior performance, with an area under the curve (AUC) of 0.780 for the validation set. According to the feature importance result, red blood cell distribution width (RDW)_mean was identified as the most influential feature. This study demonstrated the potential of leveraging ML as a viable approach for predicting mortality risk in patients with DN. An interpretable ML model exhibited strong performance that could support clinical decision-making and enhance patient prognosis to some extent.</p>

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Machine learning models predict mortality risk in diabetic neuropathy patients using MIMIC-IV data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月5日

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  • Yanyi Huang,
  • Wei Mo,
  • Yang Liu,
  • Shuanglin Wang,
  • Yanping Zeng,
  • Pingping Jiang,
  • He Wang,
  • Jianlu Bi

摘要

We aimed to construct and validate interpretable models for predicting mortality risk using machine learning (ML) methods to identify the risk factors associated with mortality in patients with diabetic neuropathy (DN). We selected patients from the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-IV)). Independent risk factors for in-hospital death were screened using Least Absolute Shrinkage and Selection Operator (LASSO). Subsequently, we constructed mortality risk prediction models utilizing random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and logistic regression (LR). Finally, we comprehensively assessed and interpreted the best-performing ML model using SHapley Additive exPlanations (SHAP) analysis. The final study enrolled 1,313 patients with DN, randomly split into training (1,050, 80%) and validation sets (263, 20%). The RF model demonstrated superior performance, with an area under the curve (AUC) of 0.780 for the validation set. According to the feature importance result, red blood cell distribution width (RDW)_mean was identified as the most influential feature. This study demonstrated the potential of leveraging ML as a viable approach for predicting mortality risk in patients with DN. An interpretable ML model exhibited strong performance that could support clinical decision-making and enhance patient prognosis to some extent.