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1
Mathematical modeling research to support the development of automated insulin-delivery systems.
J Diabetes Sci Technol. 2009 Mar 1;3(2):388-95. doi: 10.1177/193229680900300223.
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Simulation environment to evaluate closed-loop insulin delivery systems in type 1 diabetes.
J Diabetes Sci Technol. 2010 Jan 1;4(1):132-44. doi: 10.1177/193229681000400117.
3
Holistic Impact of Closed-Loop Technology on People With Type 1 Diabetes.
J Diabetes Sci Technol. 2015 Jul;9(4):932-3. doi: 10.1177/1932296815580162. Epub 2015 Apr 7.
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Update on mathematical modeling research to support the development of automated insulin delivery systems.
J Diabetes Sci Technol. 2010 May 1;4(3):759-69. doi: 10.1177/193229681000400334.
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Insulin pump safety meeting: summary report.
J Diabetes Sci Technol. 2009 Mar 1;3(2):396-402. doi: 10.1177/193229680900300224.
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In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes.
J Diabetes Sci Technol. 2009 Jan;3(1):44-55. doi: 10.1177/193229680900300106.
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[What is the current state of the artificial pancreas in diabetes care?].
Internist (Berl). 2020 Jan;61(1):102-109. doi: 10.1007/s00108-019-00713-y.
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Pharmacological aspects of closed loop insulin delivery for type 1 diabetes.
Curr Opin Pharmacol. 2017 Oct;36:29-33. doi: 10.1016/j.coph.2017.07.006. Epub 2017 Aug 10.
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Who needs an artificial pancreas? (?).
J Diabetes. 2013 Sep;5(3):254-7. doi: 10.1111/1753-0407.12052. Epub 2013 May 29.

引用本文的文献

1
Current Status and Emerging Options for Automated Insulin Delivery Systems.
Diabetes Technol Ther. 2022 May;24(5):362-371. doi: 10.1089/dia.2021.0514. Epub 2022 Mar 14.
3
Use of Automated Bolus Calculators for Diabetes Management.
Eur Endocrinol. 2013 Aug;9(2):92-95. doi: 10.17925/EE.2013.09.02.92. Epub 2013 Aug 23.
4
Ongoing Debate About Models for Artificial Pancreas Systems and In Silico Studies.
Diabetes Technol Ther. 2018 Mar;20(3):174-176. doi: 10.1089/dia.2018.0038.
5
Algorithms for a closed-loop artificial pancreas: the case for proportional-integral-derivative control.
J Diabetes Sci Technol. 2013 Nov 1;7(6):1621-31. doi: 10.1177/193229681300700623.
6
Model identification using stochastic differential equation grey-box models in diabetes.
J Diabetes Sci Technol. 2013 Mar 1;7(2):431-40. doi: 10.1177/193229681300700220.
7
The identifiable virtual patient model: comparison of simulation and clinical closed-loop study results.
J Diabetes Sci Technol. 2012 Mar 1;6(2):371-9. doi: 10.1177/193229681200600223.
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Closed-loop insulin delivery utilizing pole placement to compensate for delays in subcutaneous insulin delivery.
J Diabetes Sci Technol. 2011 Nov 1;5(6):1342-51. doi: 10.1177/193229681100500605.
10
Modeling the effects of subcutaneous insulin administration and carbohydrate consumption on blood glucose.
J Diabetes Sci Technol. 2010 Sep 1;4(5):1214-28. doi: 10.1177/193229681000400522.

本文引用的文献

2
Physical activity into the meal glucose-insulin model of type 1 diabetes: in silico studies.
J Diabetes Sci Technol. 2009 Jan;3(1):56-67. doi: 10.1177/193229680900300107.
3
Model predictive control of type 1 diabetes: an in silico trial.
J Diabetes Sci Technol. 2007 Nov;1(6):804-12. doi: 10.1177/193229680700100603.
5
Predictive monitoring for improved management of glucose levels.
J Diabetes Sci Technol. 2007 Jul;1(4):478-86. doi: 10.1177/193229680700100405.
6
In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes.
J Diabetes Sci Technol. 2009 Jan;3(1):44-55. doi: 10.1177/193229680900300106.
7
Predicting subcutaneous glucose concentration in humans: data-driven glucose modeling.
IEEE Trans Biomed Eng. 2009 Feb;56(2):246-54. doi: 10.1109/TBME.2008.2005937. Epub 2008 Sep 16.
9
Computational biology resources lack persistence and usability.
PLoS Comput Biol. 2008 Jul 18;4(7):e1000136. doi: 10.1371/journal.pcbi.1000136.

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