Data-driven Modeling for Diabetes: Diagnosis and Treatment by Vasilis Marmarelis, Georgios Mitsis

By Vasilis Marmarelis, Georgios Mitsis

This contributed quantity offers computational versions 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 numerous hormones akin to insulin, glucagon, epinephrine, norepinephrine in addition to cortisol. The provided types supply a robust diagnostic software yet can also let therapy through long term glucose rules in diabetics via closed-look model-reference keep an eye on utilizing widespread insulin infusions, that are administered via implanted programmable micro-pumps. This study quantity goals at proposing state of the art study in this topic and demonstrating the capability purposes of modeling to the prognosis and therapy of diabetes. the objective viewers essentially includes examine and specialists within the box however the booklet can also be useful for graduate students.

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Extra info for Data-driven Modeling for Diabetes: Diagnosis and Treatment

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Data-driven models have been investigated on CGM time-series alone, or by considering inputs as well. The meal sub models of [24, 62] are furthermore often used as input generating components in data-driven models to approximate the glucose flux input from the gut following a meal intake. , to be used for alarm triggering in CGM devices, or temporary insulin pump shut-off, as well as establishing models suitable for model-based control. Time-series analysis by Auto-regressive (AR) models started with [14], who evaluated the basic underlying assumptions concerning stationarity and autocovariance that AR modeling is based upon, concluding that diabetic data generally is non-stationary, but highly auto-correlated, thus recommending the models to be recurrently re-estimated.

Toffolo G, Bergman RN, Finegood DT, Bowden CR, Cobelli C (1980) Quantitative estimation of beta cell sensitivity to glucose in the intact organism: a minimal model of insulin kinetics in the dog. Diabetes 29:979–990 42. Toffolo G, Campioni M, Basu R, Rizza RA, Cobelli C (2006) A minimal model of insulin secretion and kinetics to assess hepatic insulin extraction. Am J Physiol Endocrinol Metab 290:E169–E176 43. Tresp V, Briegel T, Moody J (1999) Neural-network models for the blood glucose metabolism of a diabetic.

Z. Marmarelis Fig. 14 The closed-loop configuration representing the plasma insulin-glucose dynamic interactions (glucose-to-insulin and insulin-to-glucose models, B and A respectively). g. g. ) components, Gc(t) and Ic(t) (Figs. 11 and 13, left panels), to form the observed variables G(t) and I(t), respectively. Regarding the overall functional characteristics of the closed-loop configuration of Fig. 14, we examine the closed-loop relation in operator notation: GðtÞ ¼ A½B½Gðtފ þ Id ðtފ þ Gd ðtÞ ð26Þ where B[G(t)] denotes that the operator B (glucose-to-insulin PDM model) acts on the glucose signal G(t).

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