Personalized Predictive Modeling in Type 1 Diabetes

Personalized Predictive Modeling in Type 1 Diabetes

1st Edition - November 29, 2017

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  • Authors: Eleni Georga, Dimitrios Fotiadis, Stelios Tigas
  • Paperback ISBN: 9780128048313
  • eBook ISBN: 9780128051467

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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.

Key Features

  • 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


Bioengineers, Clinicians, graduate and undergraduate students in the field of medicine and biomedical engineering

Table of Contents

  • Chapter 1 Introduction
    1.1 The Glucose-Insulin Regulatory System
    1.2 Pathophysiology of Diabetes Mellitus
    1.3 Assessment of Glycemic Control
    1.4 Management of Diabetes
    1.5 Predictive Modelling of Glucose Concentration
    1.6 The Artificial Pancreas System
    Chapter 2 Data-Driven Prediction of Glucose Concentration in Type 1 Diabetes
    2.1 Dataset Construction
    2.1.1 Data Pre-processing
    2.1.2 Feature Extraction
    2.2 The Glucose Prediction Model
    2.2.1 Regression Models
    2.2.2 Models of Subcutaneous Insulin Absorption
    2.2.3 Models of Meal Absorption
    2.3 Training and Evaluation of the Glucose Prediction Model
    2.4 Assessing the Performance of the Glucose Prediction Model
    Chapter 3 Linear Models of Glucose Concentration
    3.1 Polynomial Models
    3.2 Autoregressive and Autoregressive Moving Average Models
    3.3 Autoregressive and Autoregressive Moving Average Models with Exogenous Inputs
    Chapter 4 Non-linear Models of Glucose Concentration
    4.1 Nonlinear Autoregressive and Autoregressive Moving Average Models with Exogenous Inputs
    4.2 Neural Networks
    4.3 Kernel-based Regression Models
    4.4 Extreme Learning Machines
    4.5 Hybrid Approaches
    Chapter 5 Prediction Models of Hypoglycaemia
    5.1 Detection of Hypoglycaemic Events in Continuous Glucose Monitoring Data
    5.2 Predictors of Hypoglycaemia
    5.3 Statistical Methods
    5.4 Methods based on Time series Analysis
    5.5 Methods based on Machine Learning
    Chapter 6 Adaptive Glucose Prediction Models
    6.1 The Stationarity Hypothesis in Glucose Prediction Problem
    6.2 Linear Models
    6.2.1 Least-Mean-Square Algorithm
    6.2.2 Recursive Least-Squares Algorithm
    6.2.3 The Latent Variable Model
    6.3 Kalman Filters
    6.4 Neural Networks
    6.4.1 Recurrent Neural Networks
    6.4.2 Sequential Extreme Learning Machines
    6.5 Kernel Adaptive Filtering
    6.5.1 Kernel Least-Mean-Square Algorithm
    6.5.2 Kernel Recursive Least-Squares Algorithm
    6.5.3 Sparse Kernel Adaptive Filters
    6.6 Hybrid Approaches
    Chapter 7 Anticipatory Mobile Systems in Diabetes
    7.1 Architecture of Anticipatory Mobile Systems
    7.1.1 Context Sensing
    7.1.2 Context Inference
    7.1.3 Context Prediction
    7.1.4 Intelligent Actioning
    7.2 Integration of Glucose Prediction Models into Anticipatory Mobile Systems
    7.3 Use of Reinforcement Learning in Diabetes Management
    Chapter 8 Conclusions and Future Trends
    8.1 Towards Personalised Diabetes Management Systems

Product details

  • No. of pages: 252
  • Language: English
  • Copyright: © Academic Press 2017
  • Published: November 29, 2017
  • Imprint: Academic Press
  • Paperback ISBN: 9780128048313
  • eBook ISBN: 9780128051467

About the Authors

Eleni Georga

Ph.D. candidate at the Department of Materials Science and Engineering, University of Ioannina, Greece

Affiliations and Expertise

Ph.D. candidate, Department of Materials Science and Engineering, University of Ioannina, Greece

Dimitrios Fotiadis

Dimitrios 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, MN, in 1990. He is currently Professor at the Department of Materials Science and Engineering, University of Ioannina, Greece, and affiliated researcher at the Biomedical Research Dept. of the Institute of Molecular Biology and Biotechnology - FORTH. He is the Director of the Unit of Medical Technology and Intelligent Information Systems, Greece. He is the member of the board of Michailideion Cardiology Center. His research interests include modeling of human tissues and organs, intelligent wearable devices for automated diagnosis and processing/analysis of biomedical data.

Affiliations and Expertise

Professor of Biomedical Engineering, Department of Materials Science and Engineering, University of Ioannina, Greece

Stelios Tigas

Stelios Tigas is awarded PhD in Endocrinology from the University of Ioannina. Stelios Tigas international experience includes various programs, contributions and participation in different countries for diverse fields of study. Stelios Tigas research interests as an Associate Professor reflect in wide range of publications in various national and international journals

Affiliations and Expertise

Assistant Professor of Endocrinology, Department of Endocrinology and Diabetes, University of Ioannina, Greece

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