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Data-driven Modeling for Diabetes

Overview of attention for book
Cover of 'Data-driven Modeling for Diabetes'

Table of Contents

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    Book Overview
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    Chapter 1 Data-Driven and Minimal-Type Compartmental Insulin-Glucose Models: Theory and Applications
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    Chapter 2 Ensemble Glucose Prediction in Insulin-Dependent Diabetes
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    Chapter 3 Hypoglycemia Prevention Using Low Glucose Suspend Systems
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    Chapter 4 Adaptive Algorithms for Personalized Diabetes Treatment
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    Chapter 5 Pitfalls in Model Identification: Examples from Glucose-Insulin Modelling
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    Chapter 6 Simulation Models for In-Silico Evaluation of Closed-Loop Insulin Delivery Systems in Type 1 Diabetes
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    Chapter 7 Simple Parameters Describing Gut Absorption and Lipid Dynamics in Relation to Glucose Metabolism During a Routine Oral Glucose Test
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    Chapter 8 Data-Driven Modeling of Diabetes Progression
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    Chapter 9 Linear Modeling and Prediction in Diabetes Physiology
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    Chapter 10 Nonlinear Modeling of the Dynamic Effects of Free Fatty Acids on Insulin Sensitivity
Attention for Chapter 5: Pitfalls in Model Identification: Examples from Glucose-Insulin Modelling
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Chapter title
Pitfalls in Model Identification: Examples from Glucose-Insulin Modelling
Chapter number 5
Book title
Data-driven Modeling for Diabetes
Published by
Springer, Berlin, Heidelberg, January 2014
DOI 10.1007/978-3-642-54464-4_5
Book ISBNs
978-3-64-254463-7, 978-3-64-254464-4
Authors

Simona Panunzi, Andrea DeGaetano, Panunzi, Simona, DeGaetano, Andrea

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