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Erschienen in:
Data-Driven and Minimal-Type Compartmental Insulin-Glucose Models: Theory and Applications Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

Linear Modeling and Prediction in Diabetes Physiology

verfasst von : Marzia Cescon, Rolf Johansson

Erschienen in: Data-driven Modeling for Diabetes

Verlag: Springer Berlin Heidelberg

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Abstract

Diabetes Mellitus is a chronic disease characterized by the inability of the organism to autonomously regulate the blood glucose level due to insulin deficiency or resistance, leading to serious health damages. The therapy is essentially based on insulin injections and depends strongly on patient daily decisions, being mainly based upon empirical experience and rules of thumb. This chapter presents work on data-driven glucose metabolism modeling and short-term, that is, up to 120 min, blood-glucose prediction in Type 1 Diabetes Mellitus (T1DM) subjects. Low-order, individualized, data-driven, stable, physiologically relevant models were identified from a population of 9 datasets from T1DM patients. Model structures include: autoregressive moving average with exogenous inputs (ARMAX) models and state-space models. The performances of the model-based predictors were compared to those achieved by the zero-order hold (ZOH).

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Vorheriges Kapitel Data-Driven Modeling of Diabetes Progression
Nächstes Kapitel Nonlinear Modeling of the Dynamic Effects of Free Fatty Acids on Insulin Sensitivity
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Metadaten
Titel
Linear Modeling and Prediction in Diabetes Physiology
verfasst von
Marzia Cescon
Rolf Johansson
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
Buch
Data-driven Modeling for Diabetes

Print ISBN: 978-3-642-54463-7

Electronic ISBN: 978-3-642-54464-4

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-642-54464-4_9

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