Personalized Predictive Modelling In Type 1 Diabetes

Paperback | July 1, 2017

byEleni I. Georga, Dimitrios I Fotiadis, Stelios K. Tigas

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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. Describes fundamentals of modeling techniques as applied to glucose control Covers model selection process and model validation Offers computer code on a companion website to show implementation of models and algorithms Features the latest developments in the field of diabetes predictive modeling

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From the Publisher

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 th...

Ph.D. candidate at the Department of Materials Science and Engineering, University of Ioannina, GreeceDimitrios I. Fotiadis received his Diploma degree in chemical engineering from National Technical University of Athens, Athens, Greece, in 1985 and the Ph.D. degree in chemical engineering from the University of Minnesota, Minneapolis,...
Format:PaperbackDimensions:300 pages, 8.75 × 6.35 × 0.68 inPublished:July 1, 2017Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:012804831X

ISBN - 13:9780128048313

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Table of Contents

1. Introduction
2. Data-Driven Prediction of Glucose Concentration in Type 1 Diabetes
3. Linear Models of Glucose Concentration
4. Non-linear Models of Glucose Concentration
5. Prediction Models of Hypoglycaemia
6. Adaptive Glucose Prediction Models
7. Anticipatory Mobile Systems in Diabetes
8. Conclusions and Future Trends