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Personalized predictive modeling in Type 1 diabetes /

Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Georga, Eleni I. (Autor), Fotiadis, Dimitrios Ioannou (Autor), Tigas, Stelios K. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, an imprint of Elsevier, [2018]
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Georga, Eleni I.,  |e author. 
245 1 0 |a Personalized predictive modeling in Type 1 diabetes /  |c Eleni I. Georga, Dimitrios I. Fotiadis, Stelios K. Tigas. 
264 1 |a London :  |b Academic Press, an imprint of Elsevier,  |c [2018] 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references. 
588 0 |a Online resource; title from PDF title page (EBSCO, viewed December, 05, 2017). 
520 |a Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii) regression technique (linear or non-linear), (iii) learning mechanism (batch or sequential), (iv) development and testing procedure and (v) scaling properties. In addition, simulation models of meal-derived glucose absorption and insulin dynamics and kinetics are covered, as an integral part of glucose predictive models. This book will help engineers and clinicians to: select a regression technique which can capture both linear and non-linear dynamics in glucose metabolism in diabetes, and which exhibits good generalization performance under stationary and non-stationary conditions; ensure the scalability of the optimization algorithm (learning mechanism) with respect to the size of the dataset, provided that multiple days of patient monitoring are needed to obtain a reliable predictive model; select a features set which efficiently represents both spatial and temporal dependencies between the input variables and the glucose concentration; select simulation models of subcutaneous insulin absorption and meal absorption; identify an appropriate validation procedure, and identify realistic performance measures. 
650 0 |a Diabetes. 
650 0 |a Glucose  |x Mathematical models. 
650 0 |a Blood glucose monitoring. 
650 0 |a Mathematical models. 
650 1 2 |a Diabetes Mellitus, Type 1  |0 (DNLM)D003922 
650 2 2 |a Blood Glucose Self-Monitoring  |0 (DNLM)D015190 
650 2 2 |a Models, Theoretical  |0 (DNLM)D008962 
650 6 |a Glucose  |0 (CaQQLa)201-0025633  |x Mod�eles math�ematiques.  |0 (CaQQLa)201-0379082 
650 6 |a Glyc�emie  |x Surveillance.  |0 (CaQQLa)201-0357383 
650 6 |a Diab�ete insulinod�ependant.  |0 (CaQQLa)201-0282015 
650 6 |a Mod�eles math�ematiques.  |0 (CaQQLa)201-0015060 
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650 7 |a MEDICAL  |x Evidence-Based Medicine.  |2 bisacsh 
650 7 |a MEDICAL  |x Internal Medicine.  |2 bisacsh 
650 7 |a Mathematical models  |2 fast  |0 (OCoLC)fst01012085 
650 7 |a Blood glucose monitoring  |2 fast  |0 (OCoLC)fst00834830 
650 7 |a Diabetes  |2 fast  |0 (OCoLC)fst00892147 
700 1 |a Fotiadis, Dimitrios Ioannou,  |e author. 
700 1 |a Tigas, Stelios K.,  |e author. 
776 0 8 |i Print version:  |a Georga, Eleni I.  |t Personalized predictive modeling in Type 1 diabetes.  |d London : Academic Press, an imprint of Elsevier, [2018]  |z 012804831X  |z 9780128048313  |w (OCoLC)960895690 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128048313  |z Texto completo