Predicting ICU Readmission in Patients With Cerebral Infarction: A Machine Learning Approach Using Neurophysiological and Clinical Data

Objective To develop and validate a machine learning (ML)‐based predictive model for intensive care unit (ICU) readmission in patients with cerebral infarction using neurophysiological and clinical data from the MIMIC‐IV database. Methods A retrospective cohort of 3,348 patients diagnosed with cerebral infarction was identified from the MIMIC‐IV database. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable logistic regression analysis. Various ML models, including Decision Tree, K‐Nearest Neighbors, LightGBM, Naïve Bayes, Random Forest, Support Vector Machine, and XGBoost, were developed and evaluated based on model performance metrics. Results The logistic regression model achieved the highest area under the receiver operating characteristic curve (AUC) of 0.682 (95% CI: 0.630–0.733). Significant predictors of ICU readmission included peptic ulcer disease, glucocorticoid use, potassium levels, and red blood cell count. Conclusions This study demonstrates that ML models can effectively predict ICU readmission in CI patients. Logistic regression provides a clinically interpretable approach for risk stratification.