Georga E - Search Results

1

Machine Learning Coronary Artery Disease Prediction Based on Imaging and Non-Imaging Data.

Kigka VI, Georga E, Tsakanikas V, Kyriakidis S, Tsompou P, Siogkas P, Michalis LK, Naka KK, Neglia D, Rocchiccioli S, Pelosi G, Fotiadis DI, Sakellarios A. Kigka VI, et al. Among authors: georga e. Diagnostics (Basel). 2022 Jun 14;12(6):1466. doi: 10.3390/diagnostics12061466. Diagnostics (Basel). 2022. PMID: 35741275 Free PMC article.

2

Data mining for blood glucose prediction and knowledge discovery in diabetic patients: the METABO diabetes modeling and management system.

Georga E, Protopappas V, Guillen A, Fico G, Ardigo D, Arredondo MT, Exarchos TP, Polyzos D, Fotiadis DI. Georga E, et al. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5633-6. doi: 10.1109/IEMBS.2009.5333635. Annu Int Conf IEEE Eng Med Biol Soc. 2009. PMID: 19964403

3

Predictive modeling of glucose metabolism using free-living data of type 1 diabetic patients.

Georga EI, Protopappas VC, Polyzos D, Fotiadis DI. Georga EI, et al. Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:589-92. doi: 10.1109/IEMBS.2010.5626374. Annu Int Conf IEEE Eng Med Biol Soc. 2010. PMID: 21095890

4

Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression.

Georga EI, Protopappas VC, Ardigo D, Marina M, Zavaroni I, Polyzos D, Fotiadis DI. Georga EI, et al. IEEE J Biomed Health Inform. 2013 Jan;17(1):71-81. doi: 10.1109/TITB.2012.2219876. Epub 2012 Sep 19. IEEE J Biomed Health Inform. 2013. PMID: 23008265

5

A predictive model of subcutaneous glucose concentration in type 1 diabetes based on Random Forests.

Georga EI, Protopappas VC, Polyzos D, Fotiadis DI. Georga EI, et al. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2889-92. doi: 10.1109/EMBC.2012.6346567. Annu Int Conf IEEE Eng Med Biol Soc. 2012. PMID: 23366528

6

A glucose model based on support vector regression for the prediction of hypoglycemic events under free-living conditions.

Georga EI, Protopappas VC, Ardigò D, Polyzos D, Fotiadis DI. Georga EI, et al. Diabetes Technol Ther. 2013 Aug;15(8):634-43. doi: 10.1089/dia.2012.0285. Epub 2013 Jul 13. Diabetes Technol Ther. 2013. PMID: 23848178

7

Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models.

Georga EI, Protopappas VC, Polyzos D, Fotiadis DI. Georga EI, et al. Med Biol Eng Comput. 2015 Dec;53(12):1305-18. doi: 10.1007/s11517-015-1263-1. Epub 2015 Mar 15. Med Biol Eng Comput. 2015. PMID: 25773366

8

Online prediction of glucose concentration in type 1 diabetes using extreme learning machines.

Georga EI, Protopappas VC, Polyzos D, Fotiadis DI. Georga EI, et al. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:3262-5. doi: 10.1109/EMBC.2015.7319088. Annu Int Conf IEEE Eng Med Biol Soc. 2015. PMID: 26736988

9

Non-linear dynamic modeling of glucose in type 1 diabetes with kernel adaptive filters.

Georga EI, Principe JC, Polyzos D, Fotiadis DI. Georga EI, et al. Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:5897-5900. doi: 10.1109/EMBC.2016.7592070. Annu Int Conf IEEE Eng Med Biol Soc. 2016. PMID: 28269596

10

Kernel-based adaptive learning improves accuracy of glucose predictive modelling in type 1 diabetes: A proof-of-concept study.

Georga EI, Principe JC, Rizos EC, Fotiadis DI. Georga EI, et al. Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:2765-2768. doi: 10.1109/EMBC.2017.8037430. Annu Int Conf IEEE Eng Med Biol Soc. 2017. PMID: 29060471

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