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Glycemic Trend Prediction Using Empirical Model Identification

Cescon, Marzia LU and Johansson, Rolf LU orcid (2009) Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference p.3501-3506
Abstract
Using methods of system identification and prediction, we investigate near-future prediction of individual specific T1DM blood glucose dynamics with the purpose of a decision-making tool development in diabetes treatment. Two strategies were approached: Firstly, Kalman estimators based on identified state-space models were designed; Secondly, direct identification of ARX- and ARMAX-based predictors was done.

Predictions over 30 minutes look-ahead were capable to track

glucose variation even in sensible ranges for estimation data,

but not on validation data.
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
subspace-based identification, biological systems
host publication
Proc. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference (CDC2009 & CCC 2009)
pages
3501 - 3506
conference name
Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference
conference location
Shanghai, China
conference dates
2009-12-16
external identifiers
  • scopus:77950836376
project
DIAdvisor
language
English
LU publication?
yes
id
273df07c-e497-4ebf-942a-4d0fb80ae4f1 (old id 1626843)
date added to LUP
2016-04-04 13:07:00
date last changed
2021-08-04 01:50:45
@inproceedings{273df07c-e497-4ebf-942a-4d0fb80ae4f1,
  abstract     = {Using methods of system identification and prediction, we investigate near-future prediction of individual specific T1DM blood glucose dynamics with the purpose of a decision-making tool development in diabetes treatment. Two strategies were approached: Firstly, Kalman estimators based on identified state-space models were designed; Secondly, direct identification of ARX- and ARMAX-based predictors was done.<br/><br>
Predictions over 30 minutes look-ahead were capable to track<br/><br>
glucose variation even in sensible ranges for estimation data,<br/><br>
but not on validation data.},
  author       = {Cescon, Marzia and Johansson, Rolf},
  booktitle    = {Proc. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference (CDC2009 & CCC 2009)},
  language     = {eng},
  pages        = {3501--3506},
  title        = {Glycemic Trend Prediction Using Empirical Model Identification},
  url          = {https://lup.lub.lu.se/search/files/62894407/8146125},
  year         = {2009},
}