Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management

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dc.contributor.authorSAÚL LANGARICA
dc.contributor.authorDIEGO DE LA VEGA
dc.contributor.authorNAWEL CARIMAN
dc.contributor.authorMARTÍN MIRANDA
dc.contributor.authorDAVID C. ANDRADE
dc.contributor.authorFELIPE NÚÑEZ
dc.contributor.authorMARIA RODRIGUEZ-FERNANDEZ
dc.date.accessioned2026-01-26T14:49:23Z
dc.date.available2026-01-26T14:49:23Z
dc.date.issued2024
dc.description.abstractAccurate short- and mid-term blood glucose predictions are crucial for patients with diabetes struggling to maintain healthy glucose levels, as well as for individuals at risk of developing the disease. Consequently, numerous efforts from the scientific community have focused on developing predictive models for glucose levels. This study harnesses physiological data collected from wearable sensors to construct a series of data-driven models based on deep learning approaches. We systematically compare these models to offer insights for practitioners and researchers venturing into glucose prediction using deep learning techniques. Key questions addressed in this work encompass the comparison of various deep learning architectures for this task, determining the optimal set of input variables for accurate glucose prediction, comparing population-wide, fine-tuned, and personalized models, and assessing the impact of an individual’s data volume on model performance. Additionally, as part of our outcomes, we introduce a meticulously curated dataset inclusive of data from both healthy individuals and those with diabetes, recorded in free-living conditions. This dataset aims to foster research in this domain and facilitate equitable comparisons among researchers.
dc.description.sponsorshipChilean National Agency for Research and Development (ANID) ACT210083 Fondecyt 1230844.
dc.identifier.doi10.1109/OJEMB.2024.3365290
dc.identifier.issn26441276
dc.identifier.urihttps://repositorioabierto.uantof.cl/handle/uantof/631
dc.language.isoen
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceIEEE Open Journal of Engineering in Medicine and Biology
dc.titleDeep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management
dc.typeArticle
oaire.citation.volume5
organization.identifier.rorUniversidad de Antofagasta
uantof.identificator.centerCentro de Investigación en Fisiología y Medicina de Altura
uantof.identificator.facultyFacultad de Ciencias de la Salud
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Centro de Fisiología y Medicina de Altura