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Continuous-time 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)
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author
organization
publishing date
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
language
English
LU publication?
yes
id
c54c4738-2591-43ee-8ca0-0202b4b0ec02 (old id 4558830)
date added to LUP
2014-07-17 13:01:33
date last changed
2017-09-17 03:30:56
@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},
  keyword      = {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-time interval model identification of blood glucose dynamics for type 1 diabetes},
  url          = {http://dx.doi.org/10.1080/00207179.2014.897004},
  volume       = {87},
  year         = {2014},
}