Personalized Predictive Modeling in Type 1 Diabetes

Book Personalized Predictive Modeling in Type 1 Diabetes Cover

Download or read online Personalized Predictive Modeling in Type 1 Diabetes PDF book by Eleni I. Georga in ePUB, PDF or Kindle eBooks. Published by Academic Press in 11 December 2017 the book become immediate popular and critical acclaim in Medical books with total hardcover page 252. Click Download Book button to get book file. Read some info about this book below.

  • Author : Eleni I. Georga
  • Publisher : Academic Press
  • Release : 11 December 2017
  • ISBN : 9780128051467
  • Page : 252 pages
  • Language : English
  • Total View : 3519 Views
  • File Size : 48,8 Mb

Personalized Predictive Modeling in Type 1 Diabetes Book PDF summary

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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