A Closed-Loop Artificial Pancreas Based on Risk Management

Cameron, Fraser; Bequette, B. Wayne; Wilson, Darrell M.; Buckingham, Bruce A.; Lee, Hyunjin; Niemeyer, G.

doi:10.1177/193229681100500226

Cited by 72 publications

(50 citation statements)

References 25 publications

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“…However, mathematical model predictions introduce uncertainty, and model individualization remains unsatisfactory for data-based models [17][18][19] or physiology-based models. To improve patient safety, this uncertainty can be quantified, [20][21][22][23] and a confidence interval can be estimated around the current glucose value so that the control actions are adapted 24 to act within the boundaries of confidence.…”

Section: Original Articlementioning

confidence: 99%

An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions

Sanz

Doyle

Dassau

2016

J Diabetes Sci Technol

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Background: Model predictive control (MPC) performance depends on the accuracy of the prediction model implemented by the controller. Complex physiology and modeling limitations often prevent the ability to provide long and accurate glucose predictions, which results in the need to account for prediction errors. Method:Optimal insulin dosage by Zone-MPC is calculated by solving an optimization problem in which a scalar index is minimized by penalizing relative input deviations and glucose predictions out of the reference zone. The controller's tuning parameters are the penalties on the input variable (insulin). Positive and negative relative inputs are penalized differently. A dynamic adaptation of the tuning parameters based on the accuracy of the model in recent history is implemented in this article and compared in silico to aggressive and conservative tunings of the same controller structure.Results: Similar average glucose and time in the safe glucose range (70-180 mg/dL) are achieved for the adaptive design and traditional controller configurations. However, percentage time under 70 mg/dL is significantly reduced, both for announced meals using bolus compensation and unannounced meals with a meal detection algorithm triggered bolus. No differences in the average insulin delivered were observed between the adaptive design and the conservative or aggressive tuning for the bolus strategy, and the adaptive controller delivered less insulin in the other scenario considered. Conclusions:The adaptive strategy provides safe and effective glucose management as well as significant reduction of hypoglycemia events. No abnormal insulin delivery profiles were observed upon the application of the adaptive strategy.

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Section: Original Articlementioning

confidence: 99%

An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions

Sanz

Doyle

Dassau

2016

J Diabetes Sci Technol

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“…Cameron et al [24] develop a multiple model probabilistic predictive control (MMPPC) approach, with meal probabilities continuously estimated to detect unannounced meals; extensions to the meal modelling approach are presented by Cameron et al [39]. In simulation studies a risk measure is minimized, also considering the uncertainty.…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 98%

“…A discrete compartmental model, which is individualized by a CF based on the total daily insulin dose, using the 1800 rule [23], is used by Cameron et al [24]. An integrating firstorder 1 dead time model relating insulin infusion to blood glucose is proposed by Percival et al [25].…”

Section: Models For Controlmentioning

confidence: 99%

Control in Physiology and Medicine

Bequette

2014

Modelling Methodology for Physiology and Medicine

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“…It can be observed that for the LMM and the MM, there is one outlier patient that corresponds to Adult 9 of the UVa standard database. This subject was previously identified as an abnormal subject [36]. The structure of these two models does not allow to identify this subject.…”

Section: Correlation Analysismentioning

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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A Closed-Loop Artificial Pancreas Based on Risk Management

Cited by 72 publications

References 25 publications

An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions

An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions

Control in Physiology and Medicine

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

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