Personalized Predictive Modeling in Type 1 Diabetes

Personalized Predictive Modeling in Type 1 Diabetes
Author: Eleni I. Georga
Publisher: Academic Press
Total Pages: 253
Release: 2017-12-11
Genre: Computers
ISBN: 0128051469
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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

Type 1 Diabetes

Type 1 Diabetes
Author: Chih-Pin Liu
Publisher: BoD – Books on Demand
Total Pages: 486
Release: 2011-11-21
Genre: Medical
ISBN: 9533077565
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This book is intended as an overview of recent progress in type 1 diabetes research worldwide, with a focus on different research areas relevant to this disease. These include: diabetes mellitus and complications, psychological aspects of diabetes, perspectives of diabetes pathogenesis, identification and monitoring of diabetes mellitus, and alternative treatments for diabetes. In preparing this book, leading investigators from several countries in these five different categories were invited to contribute a chapter to this book. We have striven for a coherent presentation of concepts based on experiments and observation from the authors own research and from existing published reports. Therefore, the materials presented in this book are expected to be up to date in each research area. While there is no doubt that this book may have omitted some important findings in diabetes field, we hope the information included in this book will be useful for both basic science and clinical investigators. We also hope that diabetes patients and their family will benefit from reading the chapters in this book.

FIRST ASSESSMENT OF THE PERFORMANCE OF A PERSONALIZED MACHINE LEARNING APPROACH TO PREDICTING BLOOD GLUCOSE LEVELS IN PATIENTS WITH TYPE 1 DIABETES: THE CDDIAB STUDY.

FIRST ASSESSMENT OF THE PERFORMANCE OF A PERSONALIZED MACHINE LEARNING APPROACH TO PREDICTING BLOOD GLUCOSE LEVELS IN PATIENTS WITH TYPE 1 DIABETES: THE CDDIAB STUDY.
Author:
Publisher:
Total Pages:
Release: 2017
Genre:
ISBN:
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BackgroundPatients with type 1 diabetes (T1D) make their decisions for insulin delivery from available past and present blood glucose (BG) data and the expected effects on BG of forthcoming meals and activities according to education rules and their own experience. Enriched information on predicted BG glucose evolution could help them in better tuning insulin therapy. CDDIAB studyu2019s objective was to evaluate a new machine learning approach to predicting BG levels of each individual from a collection of personal BG measurements with contextual data.MethodsFourteen patients with T1D (8F/6M, age: 51+/-15, T1D duration: 26+/-17 years, HbA1c: 7.09+/-0.82%), treated by insulin pump (n=11) or multiple daily insulin injections (n=3) volunteered to track BG using FreeStyle Libre (n=12), Enlite (n=1) or Dexcom G4 (n=1) CGM devices and log manually meal intakes and insulin doses for 30 days. Collected data were used to design patient-specific prediction models with 30- to 90-min horizons. The algorithms were initially fitted on a training dataset corresponding to an average of 9 days, using a 5-fold cross-validation method. The remaining days of available data were used to provide an unbiased evaluation of final models.ResultsThe MARD (Mean Absolute Relative Deviation) and the consensus Error Grid Analysis were used to evaluate accuracy of BG predictions for 30- to 90-min horizons, Our results, detailed below, show the MARD and percentage of points in zones A+B on a Parkes EGA:- At 30 minutes: MARD of 6.98%u00b12.0, and 99.93%u00b10.13,- At 60 minutes: MARD of 14.78%u00b13.25, and 98.56%u00b11.00,- At 90 minutes: MARD of 20.78%u00b14.08, and 96.29%u00b12.15.ConclusionPrediction algorithms showed promising results since 99.9, 98.6 and 96.3% of computed BG values were in EGA A+B zones at 30-, 60- and 90-min horizons, respectively. The integration into the training process of collected data by an activity tracker could further improve accuracy in future developments of the algorithm.Integrated inside a mobile application to support decision-making process, this technology could help patients anticipate and avoid upcoming occurrence of hypoglycaemia and hyperglycaemia, in particular during night time. It could also be used on top of an Artificial Pancreas MPC model, allowing for more personalization and better regulation of the system, particularly during unstable phases with rapid glucose changes.

A Personalized Algorithm to Control Blood Glucose Levels During Exercise in Individuals with Type 1 Diabetes

A Personalized Algorithm to Control Blood Glucose Levels During Exercise in Individuals with Type 1 Diabetes
Author: Milad Ghanbari
Publisher:
Total Pages: 0
Release: 2022
Genre:
ISBN:
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"Exercise has numerous well-established benefits, such as decreased risk of cardiovascular disease, improved lipid profile, and overall improved well being. These benefits are especially important to patients with type 1 diabetes, given the increased risk of cardiovascular disease in this population. Despite the established benefits of exercise, moderate intensity aerobic exercise increases the risk of hypoglycemia in individuals with type 1 diabetes, making exercise more difficult in this population. For exercise management in type 1 diabetes, carbohydrate ingestion and insulin reduction are recommended to prevent hypoglycemia. However, due to the large inter-individual variability in glucose responses to exercise, these general recommendations are not always efficient in preventing hypoglycemia. In the present thesis, a personalized closed-loop algorithm based on each patient's glucose response to exercise was developed to reduce the risk of exercise-induced hypoglycemia. The designed algorithm is based on a prediction mathematical model and uses an optimization-based method. After each exercise session, the prediction model is updated by estimating the exercise effect using a least squares algorithm. Given the updated model, an optimization problem is formulated to obtain recommendations of basal rate reduction and carbohydrate intake for the upcoming exercise session. The developed algorithm was evaluated on 100 virtual patients in a computer simulation environment. The results showed that there was a significant reduction in hypoglycemia with the developed algorithm in comparison to the conventional exercise management strategy, without significant increase in time in hyperglycemia. Furthermore, it was shown that when exercise is announced earlier, the algorithm performs better and leads to lower risk of hypoglycemia. The developed algorithm has the potential to facilitate physical activity in type 1 diabetes and thus improve quality of life. Clinical studies to assess the algorithm are warranted"--

Type 1 Diabetes

Type 1 Diabetes
Author: Ian Gallen
Publisher: Springer Science & Business Media
Total Pages: 232
Release: 2012-03-17
Genre: Biography & Autobiography
ISBN: 0857297538
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There has been a recent surge of new data on the subject of exercise and sport in type I diabetes, as well as great interest from the multidisciplinary healthcare teams looking after such patients. Providing advice and support to enable athletes to manage their diabetes during and after sport is an essential part of diabetes care. Type I Diabetes: Clinical Management of the Athlete outlines best practice and scientific progress in the management of people with type I diabetes who undertake a sport at any level. The book explores endocrine response to exercise, hypoglycemia and dietetics in the diabetic patient, and provides real-life examples of type I diabetes management at the professional athlete level. It is the first source of reference for specialists in diabetes when seeking advice on how to manage their patient and provides practical advice for equipping the type I diabetes patient with the ability to fulfill their sporting potential.

Type 1 Diabetes, An Issue of Endocrinology and Metabolism Clinics of North America

Type 1 Diabetes, An Issue of Endocrinology and Metabolism Clinics of North America
Author: Desmond A. Schatz
Publisher: Elsevier Health Sciences
Total Pages: 232
Release: 2010-09-28
Genre: Medical
ISBN: 1455700274
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This issue of Endocrinology and Metabolism Clinics of North America provides the endocrinologist with essential updates on treatment of type 1 diabetes, with an eye toward future trends and developments. The Guest Editors brought together a remarkable group of notable authors, such as Paul Robertson, President of the National Diabetes Association. Topics covered include epidemiology; economics; contemporary management; inpatient management; update on insulin pumps and continuous glucose monitoring systems; update on studies aimed at interdicting and preventing type 1 diabetes; advances in the prediction, natural history, and mechanisms leading to type 1 diabetes; complications; hypoglycemia in type 1 diabetes; new lessons from animal models; the role of the gut in the genesis of type 1 diabetes and other autoimmune diseases; and an update on transplantation for reversing type 1 diabetes.