By Vasilis Marmarelis, Georgios Mitsis
This contributed quantity provides computational versions of diabetes that quantify the dynamic interrelationships between key physiological variables implicated within the underlying body structure lower than quite a few metabolic and behavioral stipulations. those variables contain for instance blood glucose focus and numerous hormones equivalent to insulin, glucagon, epinephrine, norepinephrine in addition to cortisol. The provided versions offer a robust diagnostic instrument yet can also permit remedy through long term glucose rules in diabetics via closed-look model-reference keep an eye on utilizing widespread insulin infusions, that are administered by means of implanted programmable micro-pumps. This examine quantity goals at offering cutting-edge examine in this topic and demonstrating the capability purposes of modeling to the analysis and remedy of diabetes. the objective viewers basically contains examine and specialists within the box however the publication can also be useful for graduate students.
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Additional resources for Data-driven Modeling for Diabetes: Diagnosis and Treatment
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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 ). Note that in the more general case, the value of b could be viewed as being dependent on g, in order to account for the effect of blood glucose concentration on insulin secretion.
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