Parameters Relevance of a Glucose-Insulin Model in Type 1 Diabetes is Dependent on Meal Behavior

Glucose prediction models are essential for managing type 1 diabetes through digital therapeutics. Achieving reliable predictions requires precise calibration of key model parameters, which is non-trivial. This paper introduces a novel methodology for ranking parameters of Hovorka’s glucose-insulin model based on their influence on model output variance. It highlights how the fed-fast cycle alters parameter importance, emphasizing the need for real-time model adaptation. Calibration of selected parameters using this methodology results in a 90% and 32% reduction in Root Mean Square Error (RMSE) in simulated and real data, respectively, demonstrating the approach’s efficacy.