Linear Modeling and Prediction in Diabetes Physiology
Author
Summary, in English
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.
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.
Department/s
Publishing year
2011
Language
English
Publication/Series
Research Reports TFRT-3250
Full text
Document type
Licentiate thesis
Publisher
Department of Automatic Control, Lund Institute of Technology, Lund University
Topic
- Control Engineering
Keywords
- system identification
- prediction
- biological systems
Status
Published
Project
- DIAdvisor
Research group
- LCCC
Supervisor
ISBN/ISSN/Other
- ISSN: 0280-5316