Data-driven Modeling for Diabetes

1. Front Matter

   Pages i-x

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2. Data-Driven and Minimal-Type Compartmental Insulin-Glucose Models: Theory and Applications

   • Georgios D. Mitsis, Vasilis Z. Marmarelis

   Pages 1-35

3. Ensemble Glucose Prediction in Insulin-Dependent Diabetes

   • Fredrik Ståhl, Rolf Johansson, Eric Renard

   Pages 37-71

4. Hypoglycemia Prevention Using Low Glucose Suspend Systems

   • B. Wayne Bequette

   Pages 73-89

5. Adaptive Algorithms for Personalized Diabetes Treatment

   • Elena Daskalaki, Peter Diem, Stavroula Mougiakakou

   Pages 91-116

6. Pitfalls in Model Identification: Examples from Glucose-Insulin Modelling

   • Simona Panunzi, Andrea DeGaetano

   Pages 117-129

7. Simulation Models for In-Silico Evaluation of Closed-Loop Insulin Delivery Systems in Type 1 Diabetes

   • Malgorzata E. Wilinska, Roman Hovorka

   Pages 131-149

8. Simple Parameters Describing Gut Absorption and Lipid Dynamics in Relation to Glucose Metabolism During a Routine Oral Glucose Test

   • Andrea Tura, Giovanni Pacini

   Pages 151-163

9. Data-Driven Modeling of Diabetes Progression

   • Andrea DeGaetano, Simona Panunzi, Pasquale Palumbo, Claudio Gaz, Thomas Hardy

   Pages 165-186

10. Linear Modeling and Prediction in Diabetes Physiology

    • Marzia Cescon, Rolf Johansson

    Pages 187-222

11. Nonlinear Modeling of the Dynamic Effects of Free Fatty Acids on Insulin Sensitivity

    • Vasilis Z. Marmarelis, Dae C. Shin, Georgios D. Mitsis

    Pages 223-237

This contributed volume presents computational models of diabetes that quantify the dynamic interrelationships among key physiological variables implicated in the underlying physiology under a variety of metabolic and behavioral conditions. These variables comprise for example blood glucose concentration and various hormones such as insulin, glucagon, epinephrine, norepinephrine as well as cortisol. The presented models provide a powerful diagnostic tool but may also enable treatment via long-term glucose regulation in diabetics through closed-look model-reference control using frequent insulin infusions, which are administered by implanted programmable micro-pumps. This research volume aims at presenting state-of-the-art research on this subject and demonstrating the potential applications of modeling to the diagnosis and treatment of diabetes. The target audience primarily comprises research and experts in the field but the book may also be beneficial for graduate students.

• Computational Models of Diabetes
• Diabetes Mellitus
• Diagnostic Tool
• Implanted Micro-pumps
• Long-term Glucose Regulation
• Online Glycemic Control
• diabetes

• Biomedical Engineering, University of Southern California, Los Angeles, USA

  Vasilis Marmarelis

• Electrical and Computer Engineering, Greece, University of Cyprus,Nicosia,Greese and McGill University, Montreal QC, Canada

  Georgios Mitsis

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