Buch, Englisch, 265 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 4277 g
Design, Use and Evaluation
Buch, Englisch, 265 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 4277 g
Reihe: Lecture Notes in Bioengineering
ISBN: 978-3-319-37298-3
Verlag: Springer International Publishing
The authors address the topic of blood-glucose prediction from medical, scientific and technological points of view. Simulation studies are utilized for complementary analysis but the primary focus of this book is on real applications, using clinical data from diabetic subjects.
The text details the current state of the art by surveying prediction algorithms, and then moves beyond it with the most recent advancesin data-based modeling of glucose metabolism. The topic of performance evaluation is discussed and the relationship of clinical and technological needs and goals examined with regard to their implications for medical devices employing prediction algorithms. Practical and theoretical questions associated with such devices and their solutions are highlighted.
This book shows researchers interested in biomedical device technology and control researchers working with predictive algorithms how incorporation of predictive algorithms into the next generation of portable glucose measurement can make treatment of diabetes safer and more efficient.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Diabetologie
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
Weitere Infos & Material
From the Contents: Part I Introduction.- Clinical Relevance of Glucose Prediction: Needs and Goals.- What is the Technical Challenge of Blood Glucose Prediction?.- Part II Possible Solutions.- An Overview of Glucose Prediction Algorithms.- Data-Based Interval Models Employing Continuous-Time System Identification.