Personalized Predictive Modeling In Type 1 Diabetes
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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 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
Computational Mathematics and Modelling for Diabetes
Author: Abdesslam Boutayeb
language: en
Publisher: Springer Nature
Release Date: 2025-06-29
This book is a theoretical and pragmatic tool that applies mathematical modelling in understanding and managing diabetes and related complications such as diabetes predisposition, diabetes onset, regular glycaemic monitoring, glycated haemoglobin HbA1c, diabetes homeostasis, gestational diabetes and other associated diseases and conditions. Chapters in the book provide mathematical models dealing with the dynamics of insulin/glucose, the evolution from pre-diabetes to diabetes without and with complications, gestational diabetes and the association between diabetes and benign prostatic hyperplasia. It also applies new methods such as data mining, machine learning and deep learning. By offering pragmatic examples and comprehensive reviews on mathematical models used for diabetes, this book is useful for advanced researchers, academic teachers, students, scientists and high pharmaceutical industry executives willing to start modelling.
Neural Information Processing
The three-volume set of LNCS 11953, 11954, and 11955 constitutes the proceedings of the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. The 173 full papers presented were carefully reviewed and selected from 645 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The third volume, LNCS 11955, is organized in topical sections on semantic and graph based approaches; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.