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 numerous metabolic and behavioral stipulations. those variables contain for instance blood glucose focus and diverse hormones similar to insulin, glucagon, epinephrine, norepinephrine in addition to cortisol. The awarded types offer a robust diagnostic device 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 examine quantity goals at featuring cutting-edge study in this topic and demonstrating the capability functions of modeling to the analysis and remedy of diabetes. the objective viewers essentially includes learn and specialists within the box however the booklet can also be useful for graduate students.
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Additional resources for Data-driven Modeling for Diabetes: Diagnosis and Treatment
Model Predictive Control of Blood Glucose in Type 1 Diabetics: the Principal Dynamic Modes Approach. Proc. , Vancouver, BC, Canada, 5466-5469 2008 Data-Driven and Minimal-Type Compartmental Insulin-Glucose Models 35 28. Marmarelis VZ (1991) Wiener analysis of nonlinear feedback in sensory systems. Ann Biomed Eng 19:345–382 29. Marmarelis VZ (1997) Modeling methodology for nonlinear physiological systems. Ann Biomed Eng 25:239–251 30. Marmarelis VZ (2004) Nonlinear dynamic modeling of physiological systems.
Godsland IF, Agbaje OF, Hovorka R (2006) Evaluation of nonlinear regression approaches to estimation of insulin sensitivity by the minimal model with reference to Bayesian hierarchical analysis. Am J Physiol Endocrinol Metab 291:E167–E174 23. Krudys KM, Kahn SE, Vicini P (2006) Population approaches to estimate minimal model indexes of insulin sensitivity and glucose effectiveness using full and reduced sampling schedules. Am J Physiol Endocrinol Metab 291:E716–E723 24. Lefebvre PJ, Paolisso G, Sheen AJ, Henquin JC (1987) Pulsatility of insulin and glucagon release: physiological significance and pharmacological implications.
These issues are intertwined and their successful resolution will determine the potential utility of the advocated approach. The validity of the obtained model is based on its predictive capability and the consistency of the modeling results across different experiments. Random-like spontaneous variations enable us to perform broadband analysis of the inputoutput data and obtain reliable models that are capable of predicting the output for arbitrary inputs within the dynamic range of the available measurements, removing the restrictive effect of specialized test inputs (such as impulses or sinusoids).
Data-driven Modeling for Diabetes: Diagnosis and Treatment by Vasilis Marmarelis, Georgios Mitsis