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Personalized predictive modeling in ...

Fotiadis, Dimitrios Ioannou,

Personalized predictive modeling in Type 1 diabetes

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Personalized predictive modeling in Type 1 diabetesEleni I. Georga, Dimitrios I. Fotiadis, Stelios K. Tigas.
作者:	Georga, Eleni I.,
其他作者:	Fotiadis, Dimitrios Ioannou,
出版者:	London :Academic Press, an imprint of Elsevier,2018.
面頁冊數:	1 online resource.
標題:	Diabetes.
電子資源:	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:

274215
Diabetes.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: RC660

Dewey Class. No.: 616.462

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

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