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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

Ensemble Glucose Prediction in Insulin-Dependent Diabetes

verfasst von : Fredrik Ståhl, Rolf Johansson, Eric Renard

Erschienen in: Data-driven Modeling for Diabetes

Verlag: Springer Berlin Heidelberg

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Abstract

Real-time prediction of glucose in type 1 Diabetes Mellitus has received a considerable amount of scientific and commercial interest over the last decade. Numerous different models have been suggested using both physiological and data-driven approaches. Insulin-dependent diabetic glucose dynamics are known to be subject to time-shifting dynamics. Considering this, as well as the vast number of models developed in the literature, it is unclear if a single model can be determined to be optimal under every possible situation. This raises the question whether it is more useful to use one of the models solely, or if it is possible to gain additional prediction accuracy by combining their outcomes. Here, a novel merging approach—combining elements from both switching and averaging techniques, forming a ‘soft’ switcher in a Bayesian framework—is presented for the glucose prediction application. The method is demonstrated on both simulated and empirical data sets.

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Vorheriges Kapitel Data-Driven and Minimal-Type Compartmental Insulin-Glucose Models: Theory and Applications
Nächstes Kapitel Hypoglycemia Prevention Using Low Glucose Suspend Systems
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Metadaten
Titel
Ensemble Glucose Prediction in Insulin-Dependent Diabetes
verfasst von
Fredrik Ståhl
Rolf Johansson
Eric Renard
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_2

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