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Data-driven Modeling for Diabetes

Overview of attention for book
Data-driven Modeling for Diabetes
Springer Berlin Heidelberg

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 4: Adaptive Algorithms for Personalized Diabetes Treatment
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Citations

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4 Mendeley
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Chapter title
Adaptive Algorithms for Personalized Diabetes Treatment
Chapter number 4
Book title
Data-driven Modeling for Diabetes
Published by
Springer, Berlin, Heidelberg, January 2014
DOI 10.1007/978-3-642-54464-4_4
Book ISBNs
978-3-64-254463-7, 978-3-64-254464-4
Authors

Elena Daskalaki, Peter Diem, Stavroula Mougiakakou, Daskalaki, Elena, Diem, Peter, Mougiakakou, Stavroula

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 25%
Professor > Associate Professor 1 25%
Unknown 2 50%
Readers by discipline Count As %
Computer Science 1 25%
Engineering 1 25%
Unknown 2 50%

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