Advanced AI-Based Early Warning Framework for ICU Patient Monitoring Using Temporal Clinical Data

Intensive Care Units (ICUs) are high priority care units for patients with life threatening conditions, where many time-series data from monitoring devices, laboratory readings and vital signs are collected. These physiologic measurements should be closely monitored to detect early signs of decline, including CO2 retention, sepsis and cardiac events. Artificial Intelligence (AI) models, particularly deep learning-based architectures have the potential for superior handling of complex multi-layered and multi-dimensional datasets to aid real-time prediction and early warning. In this paper, focus on the development and application of AI-driven Early Warning Systems (EWS) for ICU patients based on time-series data. Investigate the use of RNNs, LSTM models and transformer-based sequential architectures to predict key events such as sepsis, respiratory failure and cardiac instability. The paper also discusses problems of data heterogeneity, missing values and model interpretability. Experimental studies indicate that our transformer-based models achieve superior prediction performance and earlier warning time point than the traditional LSTM models. Finally, the paper also addresses the potential inclusion of explainable AI (XAI), multimodal data fusion and real-time CDSS in ICU patient care.