Abstract
We present a digital twin framework for real-time glucose monitoring and forecasting in septic patients admitted to intensive care units (ICUs). The framework integrates advanced machine learning models trained on continuous glucose measurements with a dynamic digital twin workflow that enables rapid deployment to individual patients, ongoing model assessment, updating with newly acquired data, and personalized predictive decision support. Built on a foundation model-a pretrained time-series transformer-the digital twin adapts as new patient data become available and generates rolling near-term glucose forecasts in real time. To evaluate adaptability and computational efficiency, we deployed the pretrained model to ten septic patients and compared multiple personalization strategies, including zero-shot inference, linear probing, full fine-tuning, and staged fine-tuning. The results show that the model can be initialized and personalized for a new patient within seconds on a standard laptop while maintaining accurate glucose forecasts across varying data conditions. These findings demonstrate the feasibility of real-time digital twin personalization in resource-constrained, high-acuity clinical environments and highlight the potential of digital twins as scalable, AI-enabled platforms for continuous physiological monitoring, clinical decision support, and individualized treatment design in the ICU.