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Personalized predictive modeling in ...

Georga, Eleni I.,

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Personalized predictive modeling in Type 1 diabetes

Record Type:	Electronic resources : Monograph/item
Title/Author:	Personalized predictive modeling in Type 1 diabetes/ Eleni I. Georga, Dimitrios I. Fotiadis, Stelios K. Tigas.
Author:	Georga, Eleni I.,
other author:	Fotiadis, Dimitrios Ioannou,
Published:	London :Academic Press, an imprint of Elsevier, : 2018.,
Description:	1 online resource.
Subject:	Diabetes. -
Online resource:	https://www.sciencedirect.com/science/book/9780128048313
ISBN:	9780128051467 (electronic bk.)

Personalized predictive modeling in Type 1 diabetes
Georga, Eleni I.,

Personalized predictive modeling in Type 1 diabetes[electronic resource] /Eleni I. Georga, Dimitrios I. Fotiadis, Stelios K. Tigas. - London :Academic Press, an imprint of Elsevier,2018. - 1 online resource.

Includes bibliographical references.

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.

ISBN: 9780128051467 (electronic bk.)Subjects--Topical Terms:

544344
Diabetes.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: RC660

Dewey Class. No.: 616.462

National Library of Medicine Call No.: 2018 C-341

Personalized predictive modeling in Type 1 diabetes
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245 1 0 $a Personalized predictive modeling in Type 1 diabetes $h [electronic resource] / $c Eleni I. Georga, Dimitrios I. Fotiadis, Stelios K. Tigas.
260 $a London : $b Academic Press, an imprint of Elsevier, $c 2018.
300 $a 1 online resource.
504 $a Includes bibliographical references.
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. $3 544344
650 0 $a Glucose $x Mathematical models. $3 3508754
650 0 $a Blood sugar monitoring. $3 2041609
650 1 2 $a Diabetes Mellitus, Type 1. $3 1359671
650 2 2 $a Blood Glucose Self-Monitoring. $3 2186201
650 2 2 $a Models, Theoretical. $3 594123
650 7 $a HEALTH & FITNESS $x Diseases $x General. $2 bisacsh $3 1614811
650 7 $a MEDICAL $x Clinical Medicine. $2 bisacsh $3 1457888
650 7 $a MEDICAL $x Diseases. $2 bisacsh $3 1614812
650 7 $a MEDICAL $x Evidence-Based Medicine. $2 bisacsh $3 1615052
650 7 $a MEDICAL $x Internal Medicine. $2 bisacsh $3 1615053
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Fotiadis, Dimitrios Ioannou, $e author. $3 3508752
700 1 $a Tigas, Stelios K., $e author. $3 3508753
856 4 0 $u https://www.sciencedirect.com/science/book/9780128048313