By Vasilis Marmarelis, Georgios Mitsis
This contributed quantity offers computational types of diabetes that quantify the dynamic interrelationships between key physiological variables implicated within the underlying body structure below various metabolic and behavioral stipulations. those variables contain for instance blood glucose focus and diverse hormones reminiscent of insulin, glucagon, epinephrine, norepinephrine in addition to cortisol. The provided versions offer a strong diagnostic instrument yet can also let therapy through long term glucose law in diabetics via closed-look model-reference regulate utilizing common insulin infusions, that are administered through implanted programmable micro-pumps. This learn quantity goals at offering state of the art examine in this topic and demonstrating the aptitude functions of modeling to the prognosis and therapy of diabetes. the objective viewers essentially includes learn and specialists within the box however the ebook can also be worthwhile for graduate students.
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Sort 2 diabetes, linked to weight problems, is at the present time the commonest kind of diabetes. В it's also linked to a few different cardiovascular threat elements which represent the metabolic syndrome. В powerful administration of diabesity is important to the relief of morbidity and untimely morbidity as a result of heart problems.
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Extra info for Data-driven Modeling for Diabetes: Diagnosis and Treatment
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The best performance was achieved with the ARX and the ARMAX models. The ARX model gave a standard deviation of the prediction error of 17, 34, 46 and 56 mg/dl on average for the 30-, 60-, 90- and 120-min prediction, respectively. The corresponding results for the ARMAX model were 16, 30, 39 and 44 mg/dl. Another type of transfer function model, cast in the continuous domain, was approached in , where it was evaluated for nine type I subjects on separated meal and insulin intakes. Model parameters were determined both heuristically and by least-squares estimation.
5) of the AMM in the form of a nonlinear threshold operator. The range of values for the MM and AMM parameters is taken from the literature [3, 4, 20, 25, 41, 45]. The value of p3 was selected towards the upper limit of previously reported values in order to increase the contribution of the bilinear term, while the parameter b in Eq. (5), which determines feedback strength was selected to be larger than the value reported in  since, for the stimuli examined in the present paper, the effect of endogenous insulin was almost negligible for this latter value (it corresponds to low tolerance, obese patients ).