2026-04-13T01:48:32Zhttps://recercat.cat/oai/request
oai:recercat.cat:10256/212772026-03-18T20:08:33Zcom_2072_452955com_2072_2054col_2072_453081
RECERCAT author Noguer, Josep author Contreras, Ivan author Mujahid, Omer author Beneyto Tantiña, Aleix author Vehí, Josep 2022-06-30 In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of prediction models based on machine learning techniques. Experiments were performed on two cohorts of patients suffering from type 1 diabetes mellitus with significant differences in their clinical outcomes. In the first contribution, we have demonstrated that the chosen methodology is able to replicate the intrinsic characteristics of individual patients following the statistical distributions of the original data. Next, a second contribution demonstrates the potential of synthetic data to improve the performance of machine learning approaches by testing and comparing different prediction models for the problem of predicting nocturnal hypoglycemic events in type 1 diabetic patients. The results obtained for both generative and predictive models are quite encouraging and set a precedent in the use of generative techniques to train new machine learning modelsThis work was partially supported by the Spanish Ministry of Science and Innovation through grant [PID2019-107722RB-C22 /AEI/10.13039/501100011033]; [PID2020-117171RA-I00 funded by MCIN/AEI/10.13039/501100011033]; the Government of Catalonia under [2017SGR1551] Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Monitoratge de pacients Patient monitoring Diabetis Diabetes Aprenentatge automàtic Machine learning Intel·ligència artificial -- Aplicacions a la medicina Artificial intelligence -- Medical applications Glucèmia -- Control automàtic Blood sugar -- Automatic control Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion peer-reviewed