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Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions

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Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions

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dc.contributor.author Prendin, Francesco es_ES
dc.contributor.author Diez, José-Luís es_ES
dc.contributor.author Del Favero, Simone es_ES
dc.contributor.author Sparacino, Giovanni es_ES
dc.contributor.author Facchinetti, Andrea es_ES
dc.contributor.author Bondía Company, Jorge es_ES
dc.date.accessioned 2023-11-27T19:00:40Z
dc.date.available 2023-11-27T19:00:40Z
dc.date.issued 2022-11 es_ES
dc.identifier.uri http://hdl.handle.net/10251/200254
dc.description.abstract [EN] Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model's prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH >= 45 and the NN-X for PH >= 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH >= 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min. es_ES
dc.description.sponsorship This work was partially supported by the project "A noninvasive tattoo-based continuous GLUCOse Monitoring electronic system FOR Type-1 diabetes individuals (GLUCOMFORT)" (initiative "PRIN: Progetti di Rilevante Interesse Nazionale", call 2020, project ID: 2020X7XX2P) and through the initiative "Departments of Excellence" (Law 232/2016); Grant DPI2016-78831-C2-1-R funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe", Grant PID2019-107722RB-C21 funded by MCIN/AEI/10.13039/501100011033, and the mobility Grant PRX19/00463 funded by MCIN/AEI/10.13039/501100011033 for a research stay of J. Bondia at the University of Padova. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Type 1 diabetes es_ES
dc.subject Glucose prediction es_ES
dc.subject Fuzzy clustering es_ES
dc.subject Seasonal local models es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s22228682 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107722RB-C21/ES/SOLUCIONES A MEDIDA DEL PACIENTE PARA EL CONTROL DE GLUCOSA EN SANGRE EN DIABETES TIPO 1/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//DPI2016-78831-C2-1-R//SOLUCIONES PARA LA MEJORA DE LA EFICIENCIA Y SEGURIDAD DEL PÁNCREAS ARTIFICIAL MEDIANTE ARQUITECTURAS DE CONTROL MULTIVARIABLE TOLERANTES A FALLOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//PRX19%2F00463/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MUR//2020X7XX2P//A noninvasive tattoo-based continuous GLUCOse Monitoring electronic system FOR Type-1 diabetes individuals (GLUCOMFORT)/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.description.bibliographicCitation Prendin, F.; Diez, J.; Del Favero, S.; Sparacino, G.; Facchinetti, A.; Bondía Company, J. (2022). Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions. Sensors. 22(22):1-17. https://doi.org/10.3390/s22228682 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s22228682 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 22 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 36433278 es_ES
dc.identifier.pmcid PMC9694694 es_ES
dc.relation.pasarela S\488362 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Università degli studi di Padova es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministero dell'Università e della Ricerca es_ES
dc.subject.ods 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades es_ES


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