A Digital Twin for Tracking and Forecasting Glycemia with Septic Patients in ICUs
Journal:
medRxiv
Published Date:
May 4, 2026
Abstract
We present a digital twin framework for real time glucose monitoring and forecasting in septic patients in intensive care units (ICUs). The framework combines advanced machine learning models trained on continuous glucose measurements with a dynamic transfer learning workflow that enables rapid deployment to individual patients and supports personalized, adaptive, and predictive clinical decision making. Built on a foundation model a pretrained time series transformer the digital twin continuously updates its parameters as new patient data arrive and produces rolling near-term forecasts in real time. To assess adaptability and computational efficiency, we deployed the pretrained model to ten septic patients and evaluated multiple retraining strategies, including zero-shot inference, linear probing, and full and staged fine tuning. Results show that the model can be initialized and personalized for a new patient within seconds on a standard laptop while achieving accurate glucose forecasts under varying data conditions. These findings demonstrate the feasibility of real time model personalization in resource constrained, high acuity 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.
