Towards hypoglycemia prediction and avoidance for Type 1 Diabetic patients

MohammadRidha, Taghreed; Rivadeneira, Pablo S.; Cardelli, Michel; Magdelaine, Nicolas; Moog, Claude H.

doi:10.1109/cdc.2017.8264264

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…However, the insulin concentration and CHO absorption equations are given as second-order ODEs. In [71], a better state-space realization was performed where all variables have physiological meaning. The model was also analyzed and compared regarding the identifiability property with a subcutaneous oral glucose MM and an intensive control insulin-nutrition-glucose model in [13].…”

Section: Control-oriented Models: a Reviewmentioning

confidence: 99%

Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis

et al. 2021

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One of the main challenges of glucose control in patients with type 1 diabetes is identifying a control-oriented model that reliably predicts glycemia behavior. Here, a review is provided emphasizing the structural identifiability and observability properties, and surprisingly, it is shown that few of them are globally identifiable and observable at the same time. Consequently, a general proposal is developed to encompass four linear models according to suitable assumptions and transformations. After the corresponding structural properties analysis, two minimal model structures are generated, which are globally identifiable and observable. Then, the practical identifiability is analyzed for this application showing that the standard collected data in many cases do not have the necessary quality to ensure a unique solution in the identification process even when a considerable amount of data is collected. The two minimal control-oriented models were identified using a standard identification procedure using data from 30 virtual patients of the UVA/Padova simulator and 77 diabetes care data from adult patients of a diabetes center. The identification was performed in two stages: calibration and validation. In the first stage, the average length was taken as two days (dictated by the practical identifiability). For both structures, the mean absolute error was 16.8 mg/dl and 9.9 mg/dl for virtual patients and 21.6 mg/dl and 21.5 mg/dl for real patients. For the second stage, a one-day validation window was considered long enough for future artificial pancreas applications. The mean absolute error was 23.9 mg/dl and 12.3 mg/dl for virtual patients and 39.2 mg/dl and 36.6 mg/dl for virtual and real patients. These results confirm that linear models can be used as prediction models in model-based control strategies as predictive control.

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Section: Control-oriented Models: a Reviewmentioning

confidence: 99%

Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis

et al. 2021

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“…Some of the assumptions used for this model and argued in [8] have recently been used to update alternative models as in [18]. The model has also been used in several works [19, 20, 21].…”

Section: Hfs Control Lawmentioning

confidence: 99%

Hypoglycaemia‐free artificial pancreas project

Magdelaine

Rivadeneira

Chaillous

et al. 2020

IET Systems Biology

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Driving blood glycaemia from hyperglycaemia to euglycaemia as fast as possible while avoiding hypoglycaemia is a major problem for decades for type-1 diabetes and is solved in this study. A control algorithm is designed that guaranties hypoglycaemia avoidance for the first time both from the theory of positive systems point of view and from the most pragmatic clinical practice. The solution consists of a state feedback control law that computes the required hyperglycaemia correction bolus in real-time to safely steer glycaemia to the target. A rigorous proof is given that shows that the control-law respects the positivity of the control and of the glucose concentration error: as a result, no hypoglycaemic episode occurs. The so-called hypo-free strategy control is tested with all the UVA/Padova T1DM simulator patients (i.e. ten adults, ten adolescents, and ten children) during a fasting-night scenario and in a hybrid closed-loop scenario including three meals. The theoretical results are assessed by the simulations on a large cohort of virtual patients and encourage clinical trials.

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