Effect of Model, Observer and Their Interaction on State and Disturbance Estimation in Artificial Pancreas: An In-Silico Study

Sala-Mira, I.; Siket, Máté; Kovács, Levente; Eigner, György; Bondia, Jorge

doi:10.1109/access.2021.3120880

Cited by 11 publications

(6 citation statements)

References 46 publications

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“…The effect of the observer algorithm and model structure on the precision of estimating plasma insulin, rate of glucose appearance, and glucose levels is statistically quantified in the study. The data required for the analysis is produced [11].…”

Section: Resultsmentioning

confidence: 99%

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A Review on artificial pancreas mathematical models

Gavrila,

Balas,

Balas

2024

J. Phys.: Conf. Ser.

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The development of an artificial pancreas (AP) has been a topic of interest in the field of diabetes management for several decades. An AP system is designed to mimic the function of the pancreas by continuously monitoring blood glucose levels and delivering insulin or glucagon in response to changes in glucose concentration. Mathematical models play a crucial role in the development and evaluation of AP systems, as they enable the simulation and prediction of the system’s performance. This review paper provides an overview of the mathematical models used in AP research. The paper discusses the strengths and limitations of each type of model, as well as their applications in AP research. The review also highlights the challenges and opportunities in AP model development, such as the need for personalized models and the integration of data from multiple sources. Overall, this review provides a comprehensive understanding of the role of mathematical models in AP research and their potential for improving diabetes management.

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Section: Resultsmentioning

confidence: 99%

“…This result indicates that model parameter change offers potential for increasing estimation accuracy. In artificial pancreas systems, the research highlights the significance of precise state and disturbance estimates since it affects treatment outcomes and decision-making [10], [11].…”

Section: Resultsmentioning

confidence: 99%

A Review on artificial pancreas mathematical models

Gavrila,

Balas,

Balas

2024

J. Phys.: Conf. Ser.

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“…The Identifiable Virtual Patient model proposed by Kanderian et al [ 48 ] is then considered here for in silico analysis. The IVP is based on two previous models [ 24 , 49 ] and has already been used in [ 50 ] for the controller design and in [ 51 ] for the design of state, parameter and disturbance estimators in artificial pancreas.…”

Section: Virtual Patient Modelmentioning

confidence: 99%

Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems

Farias

Bessa

2022

Bioengineering

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Type 1 diabetes mellitus is a disease that affects millions of people around the world. Recent progress in embedded devices has allowed the development of artificial pancreas that can pump insulin subcutaneously to automatically regulate blood glucose levels in diabetic patients. In this work, a Lyapunov-based intelligent controller using artificial neural networks is proposed for application in automated insulin delivery systems. The adoption of an adaptive radial basis function network within the control scheme allows regulation of blood glucose levels without the need for a dynamic model of the system. The proposed model-free approach does not require the patient to inform when they are going to have a meal and is able to deal with inter- and intrapatient variability. To ensure safe operating conditions, the stability of the control law is rigorously addressed through a Lyapunov-like analysis. In silico analysis using virtual patients are provided to demonstrate the effectiveness of the proposed control scheme, showing its ability to maintain normoglycemia in patients with type 1 diabetes mellitus. Three different scenarios were considered: one long- and two short-term simulation studies. In the short-term analyses, 20 virtual patients were simulated for a period of 7 days, with and without prior basal therapy, while in the long-term simulation, 1 virtual patient was assessed over 63 days. The results show that the proposed approach was able to guarantee a time in the range above 95% for the target glycemia in all scenarios studied, which is in fact well above the desirable 70%. Even in the long-term analysis, the intelligent control scheme was able to keep blood glucose metrics within clinical care standards: mean blood glucose of 119.59 mg/dL with standard deviation of 32.02 mg/dL and coefficient of variation of 26.78%, all below the respective reference values.

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“…This first contribution led to three journal articles (Sala-Mira et al 2019;Sala-Mira et al 2021;Faccioli et al 2022) The module developed in the first contribution yielded acceptable results against unannounced meals; however, exercise-induced hypoglycemia would unlikely be compensated with this module due to the long offset action of insulin. Thus, a new module was designed to overcome this limitation by suggesting carbohydrate intake (Chapter 7).…”

Section: Discussionmentioning

confidence: 99%

“…In the research stay, another PhD candidate, Máté Siket, participated in the work, being the tuning of the KFs his main contribution. Therefore, in this section, I have only included a summary of the tuning procedure and more information can be found in a joint article(Sala-Mira et al 2021).…”

mentioning

confidence: 99%

New Contributions Towards Meal and Exercise Announcement-Free Artificial Pancreas Systems

Mira¹

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Effect of Model, Observer and Their Interaction on State and Disturbance Estimation in Artificial Pancreas: An In-Silico Study

Cited by 11 publications

References 46 publications

A Review on artificial pancreas mathematical models

A Review on artificial pancreas mathematical models

Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems

New Contributions Towards Meal and Exercise Announcement-Free Artificial Pancreas Systems

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