By Vasilis Marmarelis, Georgios Mitsis
This contributed quantity provides computational types of diabetes that quantify the dynamic interrelationships between key physiological variables implicated within the underlying body structure less than a number of metabolic and behavioral stipulations. those variables include for instance blood glucose focus and diverse hormones comparable to insulin, glucagon, epinephrine, norepinephrine in addition to cortisol. The provided versions offer a strong diagnostic device yet can also allow remedy through long term glucose law in diabetics via closed-look model-reference keep an eye on utilizing widespread insulin infusions, that are administered by way of implanted programmable micro-pumps. This learn quantity goals at proposing cutting-edge examine in this topic and demonstrating the aptitude purposes of modeling to the analysis and remedy of diabetes. the objective viewers basically contains examine and specialists within the box however the e-book can also be important for graduate students.
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Extra resources for Data-driven Modeling for Diabetes: Diagnosis and Treatment
Example text
A very similar model structure was used in [55], the difference being a time delay changed into a time lag. In this chapter, breakfast glucose excursion prediction was addressed for 10 patient datasets collected in the DIAdvisor project [30]. For each patient, model parameters were determined by Ensemble Glucose Prediction in Insulin-Dependent Diabetes 43 constrained least squares for two breakfast meals and cross-validated on a third breakfast, with an average fit value of 42 %. Neural network (NN) models have been shown to be a competitive approach in [26], where a recurrent NN model was compared against an AR and an ARX model on a 30 patient dataset, retrieved from the Padova simulation model.
The results provide evidence that Volterra models, free of a priori assumptions, may be estimated reliably from patient-specific data. These models may provide quantitative descriptions that reflect the underlying physiological mechanisms under general operating conditions and may prove useful in diagnostic or therapeutic [27] applications. We should note that for model-based glucose control applications, additional factors, such as the delay between plasma glucose and the sensor signal, should be taken into account.
12, since an increase of glucose is expected to elicit an increase of insulin by stimulating pancreatic secretion. In general, the second branch of both the above models (Figs. 11 and 13) appears to have a counter-regulatory role, balancing the effect of the respective first branch of each model. The model prediction achieved by the PDM model of Fig. 6 %. This implies a predominantly nonlinear relationship between glucose and insulin. The large nonlinear NMSE value suggests that spontaneous plasma glucose variations account for a fraction of spontaneous insulin variations, and that interferences from other physiological variables dominate in the formation of the insulin signal, along with the aforementioned possible effects of measurement errors.
