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Data-Driven and Minimal-Type Compartmental Insulin-Glucose Models: Theory and Applications Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Ensemble Glucose Prediction in Insulin-Dependent Diabetes

Authors : Fredrik Ståhl, Rolf Johansson, Eric Renard

Published in: Data-driven Modeling for Diabetes

Publisher: 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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previous chapter Data-Driven and Minimal-Type Compartmental Insulin-Glucose Models: Theory and Applications
next chapter Hypoglycemia Prevention Using Low Glucose Suspend Systems
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Metadata
Title
Ensemble Glucose Prediction in Insulin-Dependent Diabetes
Authors
Fredrik Ståhl
Rolf Johansson
Eric Renard
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
Book
Data-driven Modeling for Diabetes

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

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

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-642-54464-4_2