T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
20 Dec 2023
20 Dec 2023
Historique:
received:
19
05
2023
accepted:
08
11
2023
medline:
21
12
2023
pubmed:
21
12
2023
entrez:
20
12
2023
Statut:
epublish
Résumé
Type 1 diabetes mellitus (T1D) patients face daily difficulties in keeping their blood glucose levels within appropriate ranges. Several techniques and devices, such as flash glucose meters, have been developed to help T1D patients improve their quality of life. Most recently, the data collected via these devices is being used to train advanced artificial intelligence models to characterize the evolution of the disease and support its management. Data scarcity is the main challenge for generating these models, as most works use private or artificially generated datasets. For this reason, this work presents T1DiabetesGranada, an open under specific permission longitudinal dataset that not only provides continuous glucose levels, but also patient demographic and clinical information. The dataset includes 257 780 days of measurements spanning four years from 736 T1D patients from the province of Granada, Spain. This dataset advances beyond the state of the art as one the longest and largest open datasets of continuous glucose measurements, thus boosting the development of new artificial intelligence models for glucose level characterization and prediction.
Identifiants
pubmed: 38123598
doi: 10.1038/s41597-023-02737-4
pii: 10.1038/s41597-023-02737-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
916Subventions
Organisme : Ministry of Economy and Competitiveness | Instituto de Salud Carlos III (Institute of Health Carlos III)
ID : PI18-01235
Informations de copyright
© 2023. The Author(s).
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