ICU Readmission Prediction for Intracerebral Hemorrhage Patients using MIMIC III and MIMIC IV Databases
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Background
Intracerebral hemorrhage (ICH) is a critical form of stroke resulting from bleeding within the brain, with a mortality rate of 40-50% within a few days and significant risk of long-term disability. Despite the high incidence of ICU readmissions among ICH patients, the specific factors contributing to these readmissions remain unclear. This study utilizes MIMIC-III and MIMIC-IV databases to develop machine learning models that predict ICU readmissions in ICH patients.
Methods
Data from 2,144 patients were extracted using ICD-9 and ICD-10 codes. Four machine learning models - AdaBoost, Random Forest, XGBoost, and LightGBM - were implemented. Recursive Feature Elimination with Cross-Validation reduced features from 50 to 18 key predictors. The RandomUnderSampler technique addressed class imbalance by reducing majority class samples to 60% of the minority class, while the Optuna framework with Tree-structured Parzen Estimator optimized model parameters. Performance was primarily evaluated using AUROC, which effectively handles class imbalance and provides threshold-independent assessment, complemented by accuracy, sensitivity, and specificity metrics.
Results
The AdaBoost model achieved an AUROC of 0.877 (95% CI: 0.815-0.913) and accuracy of 0.810, improving from the previous best AUROC of 0.736. Sensitivity notably increased from 22.6% to 84.0%, demonstrating substantial improvement in identifying high-risk patients. This enhancement resulted from effective class imbalance handling and AdaBoost’s adaptive weighting mechanism. Through comprehensive SHAP and ablation analyses, we identified oxygen saturation and cardiovascular disease as crucial predictive features for ICU readmission risk assessment, providing new insights into patient monitoring and care management.
Conclusions
Our preprocessing methodology and model selection strategy significantly improved high-risk ICH patient identification. Through comprehensive improvements combining advanced hyperparameter optimization, balanced sampling techniques, and dual feature importance analysis, we achieved a 19.2% AUROC improvement while reducing feature dimensionality from 51 to 18. This integrated approach demonstrates the potential of machine learning in enhancing clinical decision-making. The framework provides a promising foundation for developing clinical decision support tools in ICU settings, improving resource allocation and enabling more personalized patient care interventions.
