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Linear Modeling and Prediction in Diabetes Physiology

Cescon, Marzia LU (2011)
Abstract
Diabetes Mellitus is a chronic disease characterized by the inability of the organism to autonomously regulate the blood glucose level due to insulin deficiency or resistance, leading to serious health damages. The therapy is essentially based on insulin injections and depends strongly on patient daily decisions, being mainly based upon empirical experience and rules of thumb. The development of a prediction engine capable of personalized on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise would therefore be a

valuable initiative towards an improved management of the desease.



This thesis presents work on data-driven glucose metabolism modeling and... (More)
Diabetes Mellitus is a chronic disease characterized by the inability of the organism to autonomously regulate the blood glucose level due to insulin deficiency or resistance, leading to serious health damages. The therapy is essentially based on insulin injections and depends strongly on patient daily decisions, being mainly based upon empirical experience and rules of thumb. The development of a prediction engine capable of personalized on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise would therefore be a

valuable initiative towards an improved management of the desease.



This thesis presents work on data-driven glucose metabolism modeling and short-term, that is, up to 120 minutes, blood-glucose prediction in Type 1 Diabetes Mellitus (T1DM) subjects.



In order to address model-based control for blood glucose regulation, low-order, individualized, data-driven, stable, physiological relevant

models were identified from a population of 9 T1DM patients data. Model structures include: autoregressive moving average with exogenous inputs

(ARMAX) models and state-space models.



ARMAX multi-step-ahead predictors were estimated by means of least-squares estimation; next regularization of the autoregressive coefficients was introduced. ARMAX-based predictors and zero-order hold were computed to allow comparison.



Finally, preliminary results on subspace-based multi-step-ahead multivariate predictors is presented. (Less)
Please use this url to cite or link to this publication:
author
supervisor
organization
publishing date
type
Thesis
publication status
published
subject
keywords
system identification, prediction, biological systems
publisher
Department of Automatic Control, Lund Institute of Technology, Lund University
project
DIAdvisor
language
English
LU publication?
yes
id
15437c9e-2599-424b-a09e-c26537680327 (old id 1982458)
date added to LUP
2011-06-23 12:04:22
date last changed
2016-09-19 08:44:45
@misc{15437c9e-2599-424b-a09e-c26537680327,
  abstract     = {Diabetes Mellitus is a chronic disease characterized by the inability of the organism to autonomously regulate the blood glucose level due to insulin deficiency or resistance, leading to serious health damages. The therapy is essentially based on insulin injections and depends strongly on patient daily decisions, being mainly based upon empirical experience and rules of thumb. The development of a prediction engine capable of personalized on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise would therefore be a<br/><br>
valuable initiative towards an improved management of the desease. <br/><br>
<br/><br>
This thesis presents work on data-driven glucose metabolism modeling and short-term, that is, up to 120 minutes, blood-glucose prediction in Type 1 Diabetes Mellitus (T1DM) subjects. <br/><br>
<br/><br>
In order to address model-based control for blood glucose regulation, low-order, individualized, data-driven, stable, physiological relevant<br/><br>
models were identified from a population of 9 T1DM patients data. Model structures include: autoregressive moving average with exogenous inputs<br/><br>
(ARMAX) models and state-space models.<br/><br>
<br/><br>
ARMAX multi-step-ahead predictors were estimated by means of least-squares estimation; next regularization of the autoregressive coefficients was introduced. ARMAX-based predictors and zero-order hold were computed to allow comparison.<br/><br>
<br/><br>
Finally, preliminary results on subspace-based multi-step-ahead multivariate predictors is presented.},
  author       = {Cescon, Marzia},
  keyword      = {system identification,prediction,biological systems},
  language     = {eng},
  note         = {Licentiate Thesis},
  publisher    = {Department of Automatic Control, Lund Institute of Technology, Lund University},
  title        = {Linear Modeling and Prediction in Diabetes Physiology},
  year         = {2011},
}