Glycemic Trend Prediction Using Empirical Model Identification
Cescon, Marzia LU and Johansson, Rolf LU
(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:
https://lup.lub.lu.se/record/1626843
- author
- Cescon, Marzia
LU
and Johansson, Rolf
LU
- organization
- publishing date
- 2009
- 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
- 2022-04-02 08:02:07
@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)}},
keywords = {{subspace-based identification; biological systems}},
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}},
}