Literature DB >> 25904142

GoCARB in the Context of an Artificial Pancreas.

Aristotelis Agianniotis1, Marios Anthimopoulos1, Elena Daskalaki1, Aurélie Drapela1, Christoph Stettler2, Peter Diem2, Stavroula Mougiakakou3.   

Abstract

BACKGROUND: In an artificial pancreas (AP), the meals are either manually announced or detected and their size estimated from the blood glucose level. Both methods have limitations, which result in suboptimal postprandial glucose control. The GoCARB system is designed to provide the carbohydrate content of meals and is presented within the AP framework.
METHOD: The combined use of GoCARB with a control algorithm is assessed in a series of 12 computer simulations. The simulations are defined according to the type of the control (open or closed loop), the use or not-use of GoCARB and the diabetics' skills in carbohydrate estimation.
RESULTS: For bad estimators without GoCARB, the percentage of the time spent in target range (70-180 mg/dl) during the postprandial period is 22.5% and 66.2% for open and closed loop, respectively. When the GoCARB is used, the corresponding percentages are 99.7% and 99.8%. In case of open loop, the time spent in severe hypoglycemic events (<50 mg/dl) is 33.6% without the GoCARB and is reduced to 0.0% when the GoCARB is used. In case of closed loop, the corresponding percentage is 1.4% without the GoCARB and is reduced to 0.0% with the GoCARB.
CONCLUSION: The use of GoCARB improves the control of postprandial response and glucose profiles especially in the case of open loop. However, the most efficient regulation is achieved by the combined use of the control algorithm and the GoCARB.
© 2015 Diabetes Technology Society.

Entities:  

Keywords:  artificial pancreas; carbohydrate counting; computer vision; control algorithm; type 1 diabetes

Mesh:

Substances:

Year:  2015        PMID: 25904142      PMCID: PMC4604547          DOI: 10.1177/1932296815583333

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  17 in total

1.  Can children with Type 1 diabetes and their caregivers estimate the carbohydrate content of meals and snacks?

Authors:  C E Smart; K Ross; J A Edge; B R King; P McElduff; C E Collins
Journal:  Diabet Med       Date:  2010-03       Impact factor: 4.359

2.  Standards of medical care in diabetes--2013.

Authors: 
Journal:  Diabetes Care       Date:  2013-01       Impact factor: 19.112

3.  Personalized tuning of a reinforcement learning control algorithm for glucose regulation.

Authors:  Elena Daskalaki; Peter Diem; Stavroula G Mougiakakou
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2013

4.  Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes.

Authors:  A S Brazeau; H Mircescu; K Desjardins; C Leroux; I Strychar; J M Ekoé; R Rabasa-Lhoret
Journal:  Diabetes Res Clin Pract       Date:  2012-11-10       Impact factor: 5.602

5.  An Actor-Critic based controller for glucose regulation in type 1 diabetes.

Authors:  Elena Daskalaki; Peter Diem; Stavroula G Mougiakakou
Journal:  Comput Methods Programs Biomed       Date:  2012-04-12       Impact factor: 5.428

6.  Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients.

Authors:  Elena Daskalaki; Aikaterini Prountzou; Peter Diem; Stavroula G Mougiakakou
Journal:  Diabetes Technol Ther       Date:  2011-10-12       Impact factor: 6.118

7.  Multivariable adaptive identification and control for artificial pancreas systems.

Authors:  Kamuran Turksoy; Laurie Quinn; Elizabeth Littlejohn; Ali Cinar
Journal:  IEEE Trans Biomed Eng       Date:  2014-03       Impact factor: 4.538

8.  In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes.

Authors:  Boris P Kovatchev; Marc Breton; Chiara Dalla Man; Claudio Cobelli
Journal:  J Diabetes Sci Technol       Date:  2009-01

Review 9.  The artificial pancreas: current status and future prospects in the management of diabetes.

Authors:  Thomas Peyser; Eyal Dassau; Marc Breton; Jay S Skyler
Journal:  Ann N Y Acad Sci       Date:  2014-04       Impact factor: 5.691

Review 10.  The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas.

Authors:  Melanie K Bothe; Luke Dickens; Katrin Reichel; Arn Tellmann; Björn Ellger; Martin Westphal; Ahmed A Faisal
Journal:  Expert Rev Med Devices       Date:  2013-08-23       Impact factor: 3.166

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  6 in total

Review 1.  Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions.

Authors:  Sophie Templer
Journal:  Front Endocrinol (Lausanne)       Date:  2022-06-06       Impact factor: 6.055

2.  Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App.

Authors:  Melanie B Gillingham; Zoey Li; Roy W Beck; Peter Calhoun; Jessica Castle; Mark Clements; Eyal Dassau; Francis J Doyle; Robin L Gal; Peter Jacobs; Susana R Patton; Michael R Rickels; Michael Riddell; Corby K Martin
Journal:  Diabetes Technol Ther       Date:  2020-09-29       Impact factor: 6.118

Review 3.  The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes.

Authors:  Véronique Gingras; Nadine Taleb; Amélie Roy-Fleming; Laurent Legault; Rémi Rabasa-Lhoret
Journal:  Diabetes Obes Metab       Date:  2017-08-10       Impact factor: 6.577

Review 4.  Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.

Authors:  Ivan Contreras; Josep Vehi
Journal:  J Med Internet Res       Date:  2018-05-30       Impact factor: 5.428

5.  Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes.

Authors:  Elena Daskalaki; Peter Diem; Stavroula G Mougiakakou
Journal:  PLoS One       Date:  2016-07-21       Impact factor: 3.240

6.  An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning.

Authors:  Taiyu Zhu; Kezhi Li; Lei Kuang; Pau Herrero; Pantelis Georgiou
Journal:  Sensors (Basel)       Date:  2020-09-06       Impact factor: 3.576

  6 in total

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