Physiology-Based Interval Models: A Framework for Glucose Prediction Under Intra-patient Variability

In recent years interval models, i.e., models with interval parameters, have been proposed in literature introducing new methodological approaches for glucose prediction. In this paper, a review of physiological interval models applied to the prediction of blood glucose in type 1 diabetes glycemic control is presented. Predicting blood glucose for diabetic patients is a difficult challenge mainly due to intra-patient variability and uncertainty which may jeopardize model individualization, both for data-based and physiological models. Interval models provide a theoretical framework to express the imprecision and the uncertainty related to complex systems. In the context of physiological systems, interval models can represent intra-patient variability by means of interval parameters. In this paper, interval models are introduced as well as methods for simulation. The result of an interval simulation is an envelope, which can be computed efficiently and accurately with modal interval analysis and monotone systems theories. Finally, as model predictions are as good as the individual model itself, interval model identification methods are also introduced for model individualization.