Title
Continuous-Time Interval Model Identification Of Blood Glucose Dynamics For Type 1 Diabetes
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.
Year
DOI
Venue
2014
10.1080/00207179.2014.897004
INTERNATIONAL JOURNAL OF CONTROL
Keywords
Field
DocType
interval model, identification, biomedical systems, type 1 diabetes
Pattern recognition,Simulation,Control theory,Interval model,Artificial intelligence,System identification,Type 1 diabetes,Insulin,Bolus (digestion),Mathematics
Journal
Volume
Issue
ISSN
87
7
0020-7179
Citations 
PageRank 
References 
6
0.65
9
Authors
4
Name
Order
Citations
PageRank
Harald Kirchsteiger1335.62
Rolf Johansson234962.68
Eric Renard3222.83
Luigi del Re413131.55