Estimation of secondary effect parameters in glycaemic dynamics using accumulating data from a virtual type 1 diabetic patient

Mansell, Erin J.; Docherty, Paul; Fisk, Liam; Chase, J. Geoffrey

doi:10.1016/j.mbs.2015.06.002

Cited by 11 publications

(1 citation statement)

References 47 publications

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“…A sliding mode control is presented in reference 19, which is used to stabilize the glycemic levels of a diabetic patient in the presence of parametric variations and meal disturbances. Other models have also been used for glucose‐insulin control; for example, in reference 20, a pharmacokinetic‐pharmacodynamic model is used to study the effects of stress, meals, and sleep deprivation under the effect of delayed states. In reference 21, a model‐based control of a hybrid AP capable of operating with unknown meal and exercise disturbances is presented.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based control for plasma glucose regulation in type 1 diabetes mellitus patients with unknown input delay

Targui,

Castro‐Gomez,

Hernández‐González

et al. 2024

Numer Methods Biomed Eng

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This article introduces an observer‐based control strategy tailored for regulating plasma glucose in type 1 diabetes mellitus patients, addressing challenges like unknown time‐varying delays and meal disturbances. This control strategy is based on an extended Bergman minimal model, a nonlinear glucose‐insulin model to encompass unknown inputs, such as unplanned meals, exercise disturbances, or delays. The primary contribution lies in the design of an observer‐based state feedback control in the presence of unknown long delays, which seeks to support and enhance the performance of the traditional artificial pancreas by considering realistic scenarios. The observer and control gains for the observer‐based control are computed through linear matrix inequalities formulated from Lyapunov conditions that guarantee closed‐loop stability. This design deploys a soft and gentle dynamic response, similar to a natural pancreas, despite meal disturbances and input delays. Numerical tests demonstrate the scheme's effectiveness in glycemic level regulation and hypoglycemic episode avoidance.

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Section: Introductionmentioning

confidence: 99%

Observer‐based control for plasma glucose regulation in type 1 diabetes mellitus patients with unknown input delay

Targui,

Castro‐Gomez,

Hernández‐González

et al. 2024

Numer Methods Biomed Eng

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Evaluation of pharmacokinetic model designs for subcutaneous infusion of insulin aspart

Mansell

Schmidt

Docherty

et al. 2017

J Pharmacokinet Pharmacodyn

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Effective mathematical modelling of continuous subcutaneous infusion pharmacokinetics should aid understanding and control in insulin therapy. Thorough analysis of candidate model performance is important for selecting the appropriate models. Eight candidate models for insulin pharmacokinetics included a range of modelled behaviours, parameters and complexity. The models were compared using clinical data from subjects with type 1 diabetes with continuous subcutaneous insulin infusion. Performance of the models was compared through several analyses: R for goodness of fit; the Akaike Information Criterion; a bootstrap analysis for practical identifiability; a simulation exercise for predictability. The simplest model fit poorly to the data (R = 0.53), had the highest Akaike score, and worst prediction. Goodness of fit improved with increasing model complexity (R = 0.85-0.92) but Akaike scores were similar for these models. Complexity increased practical non-identifiability, where small changes in the dataset caused large variation (CV > 10%) in identified parameters in the most complex models. Best prediction was achieved in a relatively simple model. Some model complexity was necessary to achieve good data fit but further complexity introduced practical non-identifiability and worsened prediction capability. The best model used two linear subcutaneous compartments, an interstitial and plasma compartment, and two identified variables for interstitial clearance and subcutaneous transfer rate. This model had optimal performance trade-off with reasonable fit (R = 0.85) and parameterisation, and best prediction and practical identifiability (CV < 2%).

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Shedding light on grey noise in diabetes modelling

Mansell

Docherty

Chase

2017

Biomedical Signal Processing and Control

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Estimation of secondary effect parameters in glycaemic dynamics using accumulating data from a virtual type 1 diabetic patient

Cited by 11 publications

References 47 publications

Observer‐based control for plasma glucose regulation in type 1 diabetes mellitus patients with unknown input delay

Observer‐based control for plasma glucose regulation in type 1 diabetes mellitus patients with unknown input delay

Evaluation of pharmacokinetic model designs for subcutaneous infusion of insulin aspart

Shedding light on grey noise in diabetes modelling

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