Continuous-Tme Interval Model Identification of Blood Glucose Dynamics for Type 1 Diabetes
Kirchsteiger, Harald ; Johansson, Rolf LU
; Renard, Eric
and del Re, Luigi
(2014)
In International Journal of Control
87(7).
p.1454-1466
- Abstract
- While good physiological models of the glucose metabolism in type 1 diabetic patients are well known, their parameterisation is difficult. The high intra-patient variability observed is a further major obstacle. This holds for data-based models too, so that no good patient-specific models are available. Against this background, this paper proposes the use of interval models to cover the different metabolic conditions. The control-oriented models contain a carbohydrate and insulin sensitivity factor to be used for insulin bolus calculators directly. Available clinical measurements were sampled on an irregular schedule which prompts the use of continuous-time identification, also for the direct estimation of the clinically interpretable... (More)
- While good physiological models of the glucose metabolism in type 1 diabetic patients are well known, their parameterisation is difficult. The high intra-patient variability observed is a further major obstacle. This holds for data-based models too, so that no good patient-specific models are available. Against this background, this paper proposes the use of interval models to cover the different metabolic conditions. The control-oriented models contain a carbohydrate and insulin sensitivity factor to be used for insulin bolus calculators directly. Available clinical measurements were sampled on an irregular schedule which prompts the use of continuous-time identification, also for the direct estimation of the clinically interpretable factors mentioned above. An identification method is derived and applied to real data from 28 diabetic patients. Model estimation was done on a clinical data-set, whereas validation results shown were done on an out-of-clinic, everyday life data-set. The results show that the interval model approach allows a much more regular estimation of the parameters and avoids physiologically incompatible parameter estimates. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4558830
- author
- Kirchsteiger, Harald
; Johansson, Rolf
LU
; Renard, Eric
and del Re, Luigi
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- interval model, identification, biomedical systems, type 1 diabetes
- in
- International Journal of Control
- volume
- 87
- issue
- 7
- pages
- 1454 - 1466
- publisher
- Taylor & Francis
- external identifiers
-
- wos:000335853100010
- scopus:84901016527
- ISSN
- 0020-7179
- DOI
- 10.1080/00207179.2014.897004
- project
- DIAdvisor
- language
- English
- LU publication?
- yes
- id
- c54c4738-2591-43ee-8ca0-0202b4b0ec02 (old id 4558830)
- date added to LUP
- 2016-04-01 10:18:03
- date last changed
- 2022-08-17 15:09:15
@article{c54c4738-2591-43ee-8ca0-0202b4b0ec02,
abstract = {{While good physiological models of the glucose metabolism in type 1 diabetic patients are well known, their parameterisation is difficult. The high intra-patient variability observed is a further major obstacle. This holds for data-based models too, so that no good patient-specific models are available. Against this background, this paper proposes the use of interval models to cover the different metabolic conditions. The control-oriented models contain a carbohydrate and insulin sensitivity factor to be used for insulin bolus calculators directly. Available clinical measurements were sampled on an irregular schedule which prompts the use of continuous-time identification, also for the direct estimation of the clinically interpretable factors mentioned above. An identification method is derived and applied to real data from 28 diabetic patients. Model estimation was done on a clinical data-set, whereas validation results shown were done on an out-of-clinic, everyday life data-set. The results show that the interval model approach allows a much more regular estimation of the parameters and avoids physiologically incompatible parameter estimates.}},
author = {{Kirchsteiger, Harald and Johansson, Rolf and Renard, Eric and del Re, Luigi}},
issn = {{0020-7179}},
keywords = {{interval model; identification; biomedical systems; type 1 diabetes}},
language = {{eng}},
number = {{7}},
pages = {{1454--1466}},
publisher = {{Taylor & Francis}},
series = {{International Journal of Control}},
title = {{Continuous-Tme Interval Model Identification of Blood Glucose Dynamics for Type 1 Diabetes}},
url = {{http://dx.doi.org/10.1080/00207179.2014.897004}},
doi = {{10.1080/00207179.2014.897004}},
volume = {{87}},
year = {{2014}},
}