Estimating Interval Process Models for Type 1 Diabetes for Robust Control Design

Kirchsteiger, Harald; Estrada, Giovanna Castillo; Pölzer, Stephan; Renard, Éric; Re, Luigi del

doi:10.3182/20110828-6-it-1002.03770

Cited by 33 publications

(21 citation statements)

References 10 publications

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“…Several models used for modeling and/or control can be found in the literature. For instance, Kirchsteiger et al (2011) used a third order transfer function with an integrator, van Heusden et al (2012) used a third order discrete transfer function model and Percival et al (2010) applied a first order transfer function with a time delay and an integrator. In our previous work, we used a second order transfer function model, see Boiroux et al (2012); Bátora et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Prediction Models for a Dual-Hormone Artificial Pancreas∗∗Funded by the Danish Diabetes Academy supported by the Novo Nordisk Foundation. Contact information: John Bagterp J_rgensen(jbjo@dtu.dk).

et al. 2015

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In this paper we compare the performance of five different continuous time transfer function models used in closed-loop model predictive control (MPC). These models describe the glucose-insulin and glucose-glucagon dynamics. They are discretized into a state-space description and used as prediction models in the MPC algorithm. We simulate a scenario including meals and daily variations in the model parameters. The numerical results do not show significant changes in the glucose traces for any of the models, excepted for the first order model. From the present study, we can conclude that the second order model without delay should provide the best trade-off between sensitivity to uncertainties and practical usability for in vivo clinical studies.

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Section: Introductionmentioning

confidence: 99%

Comparison of Prediction Models for a Dual-Hormone Artificial Pancreas∗∗Funded by the Danish Diabetes Academy supported by the Novo Nordisk Foundation. Contact information: John Bagterp J_rgensen(jbjo@dtu.dk).

et al. 2015

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“…By coincidence, Kirchsteiger et al used the same model as the TPM to predict BG concentrations on real patient data [30]. However, neither a comparison to other models was performed, nor a link to therapy parameters was established.…”

Section: Therapy Parameter-based Model (Tpm)mentioning

confidence: 99%

A therapy parameter-based model for predicting blood glucose concentrations in patients with type 1 diabetes

Bock

François

Gillet

2015

Computer Methods and Programs in Biomedicine

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c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 8 ( 2 0 1 5 ) [107][108][109][110][111][112][113][114][115][116][117][118][119][120][121][122][123] j o u r n a l h o m e p a g e : w w w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / c m p b However, it is widely admitted that it is almost impossible to perfectly model blood glucose dynamics while still being able to identify model parameters using only blood glucose measurements. The main contribution of this work is to propose a simple and identifiable linear dynamical model, which is based on the static prediction model of standard therapy. It is shown that the model parameters are intrinsically correlated with physician-set therapy parameters and that the reduction of the number of model parameters to identify leads to inferior data fits but to equivalent or slightly improved prediction capabilities compared to state-of-the-art models: a sign of an appropriate model structure and superior reliability.The validation of the proposed dynamic model is performed using data from the UVa simulator and real clinical data, and potential uses of the proposed model for state estimation and BG control are discussed.

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“…The reason for choosing this specific transfer function for our simulation example is explained in the following section. In [17] it was shown that a reduced order linear transfer function of the form (13) with the inputs u (1) (t) representing the carbohydrate amount ingestion and u (2) (t) the fast acting insulin quantity injected in the subcutaneous tissue is well suited to capture the main blood glucose dynamics after breakfast. Furthermore the parameter K 1 is directly linked to the carbohydrate sensitivity-the BG change after a 1g carbohydrate meal-and K 2 to the insulin sensitivity-the BG change after injection of 1 insulin unit-two parameters that are of uttermost importance for calculating the correct insulin advice for diabetes therapy.…”

Section: Simulation Examplementioning

confidence: 99%

“…Details on the clinical protocol, subjects included, datasets and measurement devices can be found in [17]. As model inputs, we used directly the information coming from the patient, the amount of carbohydrates of the meals and the quantity of insulin injected.…”

Section: Simulation Examplementioning

confidence: 99%

Direct continuous time system identification of MISO transfer function models applied to type 1 diabetes

Kirchsteiger

Pölzer

Johansson

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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Estimating Interval Process Models for Type 1 Diabetes for Robust Control Design

Cited by 33 publications

References 10 publications

Comparison of Prediction Models for a Dual-Hormone Artificial Pancreas∗∗Funded by the Danish Diabetes Academy supported by the Novo Nordisk Foundation. Contact information: John Bagterp J_rgensen(jbjo@dtu.dk).

Comparison of Prediction Models for a Dual-Hormone Artificial Pancreas∗∗Funded by the Danish Diabetes Academy supported by the Novo Nordisk Foundation. Contact information: John Bagterp J_rgensen(jbjo@dtu.dk).

A therapy parameter-based model for predicting blood glucose concentrations in patients with type 1 diabetes

Direct continuous time system identification of MISO transfer function models applied to type 1 diabetes

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