Predicting ICU Readmission Afte Intracerebral Hemorrhage: A Deep Learning Framework Using MIMIC Time-Series Data

Abstract

Intensive Care Unit (ICU) readmissions following Intracerebral Hemorrhage (ICH) are associated with increased mortality and resource burden. Current prediction models predominantly rely on static admission features, failing to capture the temporal evolution of physiological instability. This study proposes a novel deep learning framework to predict ICU readmission by leveraging high-resolution time-series data from the MIMIC-III and MIMIC-IV databases. We developed a Stacked Gated Recurrent Unit (GRU) Architecture Ensemble, integrated with Time-series Generative Adversarial Networks (TimeGAN) to address the inherent class imbalance of readmission events. Our model achieved a state-of-the-art Area Under the Receiver Operating Characteristic Curve (AUC) of 0.912, significantly outperforming traditional machine learning baselines and static feature models. The sensitivity of 88.1% highlights the model's efficacy in minimizing unsafe premature discharges. Furthermore, interpretability analysis using SHAP values identified Length of Stay, MELD Score, and Monocytes as critical predictors, revealing that readmission risk is driven by a complex interplay between systemic organ dysfunction and inflammatory response. These findings demonstrate that incorporating temporal dynamics and generative data augmentation significantly enhances risk stratification, offering a robust clinical decision support tool to optimize discharge timing in neurocritical care.