Ivan Contreras
Hivatkozott rá
Hivatkozott rá
Artificial intelligence for diabetes management and decision support: literature review
I Contreras, J Vehi
Journal of medical Internet research 20 (5), e10775, 2018
Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning
J Vehí, I Contreras, S Oviedo, L Biagi, A Bertachi
Health informatics journal 26 (1), 703-718, 2020
Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models
I Contreras, S Oviedo, M Vettoretti, R Visentin, J Vehí
PloS one 12 (11), e0187754, 2017
Machine learning techniques for hypoglycemia prediction: trends and challenges
O Mujahid, I Contreras, J Vehi
Sensors 21 (2), 546, 2021
Prediction of Blood Glucose Levels And Nocturnal Hypoglycemia Using Physiological Models and Artificial Neural Networks.
A Bertachi, L Biagi, I Contreras, N Luo, J Vehí
KDH@ IJCAI, 85-90, 2018
Prediction of nocturnal hypoglycemia in adults with type 1 diabetes under multiple daily injections using continuous glucose monitoring and physical activity monitor
A Bertachi, C Viñals, L Biagi, I Contreras, J Vehí, I Conget, M Giménez
Sensors 20 (6), 1705, 2020
Risk-based postprandial hypoglycemia forecasting using supervised learning
S Oviedo, I Contreras, C Quirós, M Giménez, I Conget, J Vehi
International journal of medical informatics 126, 1-8, 2019
Using Grammatical Evolution to Generate Short-term Blood Glucose Prediction Models.
I Contreras, A Bertachi, L Biagi, J Vehí, S Oviedo
KDH@ IJCAI, 91-96, 2018
Impact of use frequency of a mobile diabetes management app on blood glucose control: evaluation study
J Vehi, JR Isern, A Parcerisas, R Calm, I Contreras
JMIR mHealth and uHealth 7 (3), e11933, 2019
Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques
S Oviedo, I Contreras, A Bertachi, C Quirós, M Giménez, I Conget, J Vehi
Computer Methods and Programs in Biomedicine 178, 175-180, 2019
Matching island topologies to problem structure in parallel evolutionary algorithms
I Arnaldo, I Contreras, D Millán-Ruiz, JI Hidalgo, N Krasnogor
Soft Computing 17, 1209-1225, 2013
Profiling intra-patient type I diabetes behaviors
I Contreras, C Quirós, M Giménez, I Conget, J Vehi
Computer methods and programs in biomedicine 136, 131-141, 2016
A GA combining technical and fundamental analysis for trading the stock market
I Contreras, JI Hidalgo, L Núñez-Letamendia
Applications of Evolutionary Computation: EvoApplications 2012: EvoCOMNET …, 2012
Using a gpu-cpu architecture to speed up a ga-based real-time system for trading the stock market
I Contreras, Y Jiang, JI Hidalgo, L Núñez-Letamendia
Soft Computing 16, 203-215, 2012
A machine learning approach to minimize nocturnal hypoglycemic events in type 1 diabetic patients under multiple doses of insulin
A Parcerisas, I Contreras, A Delecourt, A Bertachi, A Beneyto, I Conget, ...
Sensors 22 (4), 1665, 2022
Generation of individualized synthetic data for augmentation of the type 1 diabetes data sets using deep learning models
J Noguer, I Contreras, O Mujahid, A Beneyto, J Vehi
Sensors 22 (13), 4944, 2022
Artificial Intelligence in Precision Health: From Concept to Applications
D Barh
Elsevier - Academic Press, 2020
A hybrid clustering prediction for type 1 diabetes aid: towards decision support systems based upon scenario profile analysis
I Contreras, J Vehí, R Visentin, M Vettoretti
2017 IEEE/ACM International Conference on Connected Health: Applications …, 2017
Mid-term prediction of blood glucose from continuous glucose sensors, meal information and administered insulin
I Contreras, J Vehi
XIV Mediterranean Conference on Medical and Biological Engineering and …, 2016
Blind optimisation problem instance classification via enhanced universal similarity metric
I Contreras, I Arnaldo, N Krasnogor, JI Hidalgo
Memetic Computing 6, 263-276, 2014
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Cikkek 1–20