Adaptive subspace-based prediction of T1DM glycemia

Blood glucose levels fluctuate widely in Type 1 diabetic patients expecially during stressful situations, intercurrent illness, exercise and changes in meal composition. Furthermore, inter- and intra-subject variability make the prediction of such fluctuations an even harder task. The paper deals with the application of online data-driven multi-step subspace-based patient-specific predictor models to T1DM glycemia prediction, exploiting the interplay between previously injected insulin, meal intake and eventually vital signs. When the unknown underlying model is changing over time we believe such an adaptive scheme may constitute a valuable step towards the development of an advisory tool capable of informing the patient at any time about the evolution of glycemia and possibly give advices on the most appropriate control action to take [1].