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Modeling and Prediction in Diabetes Physiology

Cescon, Marzia LU (2013) In PhD Thesis TFRT-1099
Abstract
Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate glucose control is associated with damage, dysfunction and failure of various organs. The management of the disease is non trivial and demanding. With today’s standards of current diabetes care, good glucose regulation needs

constant attention and decision-making by the individuals with diabetes. Empowering the patients with a decision support system would, therefore, improve their quality of life without additional burdens nor replacing human expertise. This thesis... (More)
Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate glucose control is associated with damage, dysfunction and failure of various organs. The management of the disease is non trivial and demanding. With today’s standards of current diabetes care, good glucose regulation needs

constant attention and decision-making by the individuals with diabetes. Empowering the patients with a decision support system would, therefore, improve their quality of life without additional burdens nor replacing human expertise. This thesis investigates the use of data-driven techniques to the purpose of glucose metabolism modeling and short-term blood-glucose predictions in Type I Diabetes Mellitus (T1DM). The goal was to use models and predictors in an advisory tool able to produce personalized short-term blood glucose predictions and on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise, to help diabetic subjects maintaining glycemia as close to normal as possible. The approaches taken to describe the glucose metabolism were discrete-time and continuous-time models on input-output form and statespace form, while the blood glucose short-term predictors, i.e., up to 120 minutes ahead, used ARX-, ARMAX- and subspace-based prediction. (Less)
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author
supervisor
opponent
  • Lovera, Marco, Politecnico di Milano, Italy
organization
publishing date
type
Thesis
publication status
published
subject
in
PhD Thesis TFRT-1099
pages
215 pages
publisher
Department of Automatic Control, Lund Institute of Technology, Lund University
defense location
Lecture hall M:B, M-building, Ole Römers väg 1, Lund University Faculty of Engineering
defense date
2013-11-29 10:15:00
ISSN
0280-5316
0280-5316
ISBN
978-91-7473-770-7
project
DIAdvisor
language
English
LU publication?
yes
id
f2fbbe70-557e-444f-90ad-6e197faf5dbe (old id 4145031)
date added to LUP
2016-04-01 13:42:18
date last changed
2023-04-18 20:31:04
@phdthesis{f2fbbe70-557e-444f-90ad-6e197faf5dbe,
  abstract     = {{Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate glucose control is associated with damage, dysfunction and failure of various organs. The management of the disease is non trivial and demanding. With today’s standards of current diabetes care, good glucose regulation needs<br/><br>
constant attention and decision-making by the individuals with diabetes. Empowering the patients with a decision support system would, therefore, improve their quality of life without additional burdens nor replacing human expertise. This thesis investigates the use of data-driven techniques to the purpose of glucose metabolism modeling and short-term blood-glucose predictions in Type I Diabetes Mellitus (T1DM). The goal was to use models and predictors in an advisory tool able to produce personalized short-term blood glucose predictions and on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise, to help diabetic subjects maintaining glycemia as close to normal as possible. The approaches taken to describe the glucose metabolism were discrete-time and continuous-time models on input-output form and statespace form, while the blood glucose short-term predictors, i.e., up to 120 minutes ahead, used ARX-, ARMAX- and subspace-based prediction.}},
  author       = {{Cescon, Marzia}},
  isbn         = {{978-91-7473-770-7}},
  issn         = {{0280-5316}},
  language     = {{eng}},
  publisher    = {{Department of Automatic Control, Lund Institute of Technology, Lund University}},
  school       = {{Lund University}},
  series       = {{PhD Thesis TFRT-1099}},
  title        = {{Modeling and Prediction in Diabetes Physiology}},
  url          = {{https://lup.lub.lu.se/search/files/3541221/4145058.pdf}},
  year         = {{2013}},
}