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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

Pitfalls in Model Identification: Examples from Glucose-Insulin Modelling

verfasst von : Simona Panunzi, Andrea DeGaetano

Erschienen in: Data-driven Modeling for Diabetes

Verlag: Springer Berlin Heidelberg

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Abstract

Two important statistical parameter estimation pitfalls, examples of which can be found in the literature, are here reviewed and discussed. The first concerns the lack of model qualitative behaviour analysis before proceeding to the actual parameter estimation phase: this may give rise in the worst cases to aberrant model behaviour and to meaningless parameter estimates. The second concerns the use of interpolated noisy observations taken to represent the real input or driving variable into a model: this gives rise to the artifactual reproduction of meaningful features of the output variables, based on data errors and hence inherently non-reproducible. This is particularly dangerous when using noisy observations instead of model predictions in coupled systems. Examples of these pitfalls are drawn from existing glucose-insulin modelling literature and recommendations are made.

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Vorheriges Kapitel Adaptive Algorithms for Personalized Diabetes Treatment
Nächstes Kapitel Simulation Models for In-Silico Evaluation of Closed-Loop Insulin Delivery Systems in Type 1 Diabetes
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Metadaten
Titel
Pitfalls in Model Identification: Examples from Glucose-Insulin Modelling
verfasst von
Simona Panunzi
Andrea DeGaetano
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_5

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