Abstract-Type 1 Patients with Diabetes (Type 1 PwDs) have to frequently adjust their insulin dosage to keep their Blood Glucose concentration (BG) within normal bounds. Meal intakes represent the most important disturbance that has to be accounted for. Its effect differs for every individual as well as for every meal. These specificities are automatically taken into account in the approach proposed in this paper. Model parameters are identified for every couple (Patient, Meal) of interest and optimal control is applied to generate individualized meal specific insulin profiles. The method does not require the use of a continuous BG meter, the profiles being infused in an open-loop manner. Results from a preliminary clinical study are presented. The concept is shown to be effective, despite limitations due to the aggressive execution chosen. Improvements are proposed and a possible pratical implementation is described.

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 8 ( 2 0 1 5 ) [107][108][109][110][111][112][113][114][115][116][117][118][119][120][121][122][123] j o u r n a l h o m e p a g e : w w w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / c m p b However, it is widely admitted that it is almost impossible to perfectly model blood glucose dynamics while still being able to identify model parameters using only blood glucose measurements. The main contribution of this work is to propose a simple and identifiable linear dynamical model, which is based on the static prediction model of standard therapy. It is shown that the model parameters are intrinsically correlated with physician-set therapy parameters and that the reduction of the number of model parameters to identify leads to inferior data fits but to equivalent or slightly improved prediction capabilities compared to state-of-the-art models: a sign of an appropriate model structure and superior reliability.The validation of the proposed dynamic model is performed using data from the UVa simulator and real clinical data, and potential uses of the proposed model for state estimation and BG control are discussed.

For patients with type 1 diabetes mellitus, appropriate control of blood glucose concentrations is vital. Exercise is one of the disturbances that can affect these concentrations. Therefore, predictions in the presence of exercise are useful among others for model-based control methods, bolus calculators and educational tools. Although several models quantifying the effect of exercise are available, they generally include a high number of model parameters, which makes the identification a particularly challenging task, especially if only blood glucose measurements are available. In this paper, a new data-based minimal extension for existing models of the glucoregulatory system, which is able to account for the effect of exercise, is proposed. As observed from clinical data, for given exercise intensities and durations, the model does not depend on exercise intensity, making intensity measurements obsolete. Another main advantage is that this minimal extension involves the identification of only two additional scalar model parameters. The resulting model shows good agreement with the clinical data, and the obtained parameters are consistent between patients.

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