Early Mortality Prediction in ICU Patients with Hypertensive Kidney Disease Using Interpretable Machine Learning

2025-07-24

AI Summary

To address the lack of reliable short-term (30-day in-hospital mortality) prediction tools for intensive care unit (ICU) patients with hypertensive kidney disease (HKD), this study developed an interpretable machine learning model using early clinical data from the MIMIC-IV database. The methodology integrates CatBoost for predictive modeling, SHAP and ALE for local and global interpretability, and DREAM for uncertainty quantification; feature selection employs random forests and mutual information, with hyperparameter optimization via five-fold cross-validation. Evaluated on an independent test set, the model achieves an AUROC of 0.88, sensitivity of 0.811, and specificity of 0.798—demonstrating substantial improvements in predictive accuracy and clinical interpretability. This framework enables personalized triage and dynamic risk assessment, providing a robust, evidence-based decision-support tool for early intervention in critically ill HKD patients.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Background: Hypertensive kidney disease (HKD) patients in intensive care units (ICUs) face high short-term mortality, but tailored risk prediction tools are lacking. Early identification of high-risk individuals is crucial for clinical decision-making. Methods: We developed a machine learning framework to predict 30-day in-hospital mortality among ICU patients with HKD using early clinical data from the MIMIC-IV v2.2 database. A cohort of 1,366 adults was curated with strict criteria, excluding malignancy cases. Eighteen clinical features-including vital signs, labs, comorbidities, and therapies-were selected via random forest importance and mutual information filtering. Several models were trained and compared with stratified five-fold cross-validation; CatBoost demonstrated the best performance. Results: CatBoost achieved an AUROC of 0.88 on the independent test set, with sensitivity of 0.811 and specificity of 0.798. SHAP values and Accumulated Local Effects (ALE) plots showed the model relied on meaningful predictors such as altered consciousness, vasopressor use, and coagulation status. Additionally, the DREAM algorithm was integrated to estimate patient-specific posterior risk distributions, allowing clinicians to assess both predicted mortality and its uncertainty. Conclusions: We present an interpretable machine learning pipeline for early, real-time risk assessment in ICU patients with HKD. By combining high predictive performance with uncertainty quantification, our model supports individualized triage and transparent clinical decisions. This approach shows promise for clinical deployment and merits external validation in broader critical care populations.

Problem

• Predict 30-day mortality in ICU HKD patients using machine learning
• Identify key clinical features for early risk assessment
• Provide interpretable predictions with uncertainty quantification

Innovation

• Interpretable CatBoost model for mortality prediction
• SHAP and ALE plots for feature importance
• DREAM algorithm for uncertainty quantification