Data augmentation and evolutionary algorithms to improve the prediction of blood glucose levels in scarcity of training data

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{velasco:2017:CEC,

• author = "Jose Manuel Velasco and Oscar Garnica and Sergio Contador and Juan Lanchares and Esther Maqueda and Marta Botella and J. Ignacio Hidalgo",
• booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Data augmentation and evolutionary algorithms to improve the prediction of blood glucose levels in scarcity of training data",
• year = "2017",
• editor = "Jose A. Lozano",
• pages = "2193--2200",
• address = "Donostia, San Sebastian, Spain",
• publisher = "IEEE",
• month = "5-8 " # jun,
• keywords = "genetic algorithms, genetic programming, grammatical evolution, biochemistry, blood, diseases, evolutionary computation, learning (artificial intelligence), medical computing, pattern classification, sugar, artificial pancreas systems, blood glucose control, blood glucose level prediction, classification system, data augmentation, data collection, diabetes mellitus type 1 patients, evolutionary algorithms, grammatical evolution model, insulin bolus sizes, patient response, personal factors, scenario selection, training data scarcity, Data models, Grammar, Insulin, Predictive models, Time series analysis",
• isbn13 = "978-1-5090-4601-0",
• DOI = "doi:10.1109/CEC.2017.7969570",
• abstract = "Diabetes Mellitus Type 1 patients are waiting for the arrival of the Artificial Pancreas. Artificial Pancreas systems will control the blood glucose of patients, improving their quality of life and reducing the risks they face daily. At the core of the Artificial Pancreas, an algorithm will forecast future glucose levels and estimate insulin bolus sizes. Grammatical Evolution has been proved as a suitable algorithm for predicting glucose levels. Nevertheless, one of the main obstacles that researches have found for training the Grammatical Evolution models is the lack of significant amounts of data. As in many other fields in medicine, the collection of data from real patients is very complex along with the fact that the patient's response can vary in a high degree due to a lot of personal factors which can be seen as different scenarios. In this paper, we propose both a classification system for scenario selection and a data augmentation algorithm that generates synthetic glucose time series from real data. Our experimental results show that, in a scarce data context, Grammatical Evolution models can get more accurate and robust predictions using scenario selection and data augmentation.",
• notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969570}",

}

Genetic Programming entries for Jose Manuel Velasco Cabo Oscar Garnica Sergio Contador J Lanchares Esther Maqueda Marta Botella-Serrano Jose Ignacio Hidalgo Perez

Citations