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Items: 1 to 20 of 85

1.

Predictive monitoring for improved management of glucose levels.

Reifman J, Rajaraman S, Gribok A, Ward WK.

J Diabetes Sci Technol. 2007 Jul;1(4):478-86.

2.

Experimental evaluation of a recursive model identification technique for type 1 diabetes.

Finan DA, Doyle FJ 3rd, Palerm CC, Bevier WC, Zisser HC, Jovanovic L, Seborg DE.

J Diabetes Sci Technol. 2009 Sep 1;3(5):1192-202.

3.

Universal glucose models for predicting subcutaneous glucose concentration in humans.

Gani A, Gribok AV, Lu Y, Ward WK, Vigersky RA, Reifman J.

IEEE Trans Inf Technol Biomed. 2010 Jan;14(1):157-65. doi: 10.1109/TITB.2009.2034141. Epub 2009 Oct 23.

PMID:
19858035
4.

Predicting subcutaneous glucose concentration in humans: data-driven glucose modeling.

Gani A, Gribok AV, Rajaraman S, Ward WK, Reifman J.

IEEE Trans Biomed Eng. 2009 Feb;56(2):246-54. doi: 10.1109/TBME.2008.2005937. Epub 2008 Sep 16.

PMID:
19272928
5.

Estimation of future glucose concentrations with subject-specific recursive linear models.

Eren-Oruklu M, Cinar A, Quinn L, Smith D.

Diabetes Technol Ther. 2009 Apr;11(4):243-53. doi: 10.1089/dia.2008.0065.

6.

The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients.

Lu Y, Gribok AV, Ward WK, Reifman J.

IEEE Trans Biomed Eng. 2010 Aug;57(8):1839-46. doi: 10.1109/TBME.2010.2047504. Epub 2010 Apr 15.

PMID:
20403780
7.

Glucose Prediction Algorithms from Continuous Monitoring Data: Assessment of Accuracy via Continuous Glucose Error-Grid Analysis.

Zanderigo F, Sparacino G, Kovatchev B, Cobelli C.

J Diabetes Sci Technol. 2007 Sep;1(5):645-51.

8.

Development of a neural network for prediction of glucose concentration in type 1 diabetes patients.

Pappada SM, Cameron BD, Rosman PM.

J Diabetes Sci Technol. 2008 Sep;2(5):792-801.

9.

Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Pérez-Gandía C, Facchinetti A, Sparacino G, Cobelli C, Gómez EJ, Rigla M, de Leiva A, Hernando ME.

Diabetes Technol Ther. 2010 Jan;12(1):81-8. doi: 10.1089/dia.2009.0076.

PMID:
20082589
10.

Predicting subcutaneous glucose concentration using a latent-variable-based statistical method for type 1 diabetes mellitus.

Zhao C, Dassau E, Jovanovič L, Zisser HC, Doyle FJ 3rd, Seborg DE.

J Diabetes Sci Technol. 2012 May 1;6(3):617-33.

11.

Diabetes technology and treatments in the paediatric age group.

Shalitin S, Peter Chase H.

Int J Clin Pract Suppl. 2011 Feb;(170):76-82. doi: 10.1111/j.1742-1241.2010.02582.x. Review.

PMID:
21323816
12.

Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes.

Pappada SM, Cameron BD, Rosman PM, Bourey RE, Papadimos TJ, Olorunto W, Borst MJ.

Diabetes Technol Ther. 2011 Feb;13(2):135-41. doi: 10.1089/dia.2010.0104.

PMID:
21284480
13.

Integrated sensor-augmented pump therapy systems [the MiniMed® Paradigm™ Veo system and the Vibe™ and G4® PLATINUM CGM (continuous glucose monitoring) system] for managing blood glucose levels in type 1 diabetes: a systematic review and economic evaluation.

Riemsma R, Corro Ramos I, Birnie R, Büyükkaramikli N, Armstrong N, Ryder S, Duffy S, Worthy G, Al M, Severens J, Kleijnen J.

Health Technol Assess. 2016 Feb;20(17):v-xxxi, 1-251. doi: 10.3310/hta20170. Review.

15.

Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor.

Zhou T, Dickson JL, Geoffrey Chase J.

J Diabetes Sci Technol. 2017 Jul 1:1932296817719089. doi: 10.1177/1932296817719089. [Epub ahead of print]

PMID:
28707484
16.

Predicting human subcutaneous glucose concentration in real time: a universal data-driven approach.

Lu Y, Rajaraman S, Ward WK, Vigersky RA, Reifman J.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:7945-8. doi: 10.1109/IEMBS.2011.6091959.

PMID:
22256183
17.

Taking a Closer Look--Continuous Glucose Monitoring in Non-Critically Ill Hospitalized Patients with Type 2 Diabetes Mellitus Under Basal-Bolus Insulin Therapy.

Schaupp L, Donsa K, Neubauer KM, Mader JK, Aberer F, Höll B, Spat S, Augustin T, Beck P, Pieber TR, Plank J.

Diabetes Technol Ther. 2015 Sep;17(9):611-8. doi: 10.1089/dia.2014.0343. Epub 2015 Apr 30.

PMID:
25927357
18.

Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series.

Sparacino G, Zanderigo F, Corazza S, Maran A, Facchinetti A, Cobelli C.

IEEE Trans Biomed Eng. 2007 May;54(5):931-7.

PMID:
17518291
19.

Home blood glucose prediction: clinical feasibility and validation in islet cell transplantation candidates.

Albisser AM, Baidal D, Alejandro R, Ricordi C.

Diabetologia. 2005 Jul;48(7):1273-9. Epub 2005 Jun 3.

PMID:
15933858
20.

Continuous glucose monitoring systems for type 1 diabetes mellitus.

Langendam M, Luijf YM, Hooft L, Devries JH, Mudde AH, Scholten RJ.

Cochrane Database Syst Rev. 2012 Jan 18;1:CD008101. doi: 10.1002/14651858.CD008101.pub2. Review.

PMID:
22258980

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