@misc{10481/91561,
year = {2023},
month = {12},
url = {https://hdl.handle.net/10481/91561},
abstract = {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.},
organization = {University of Granada within the framework of the Development Cooperation
Fund},
organization = {Andalusian Ministry of Economic Transformation, Industry,
Knowledge and Universities under grant P20_00163},
organization = {Instituto de Salud Carlos III
grants (PI18–01235), co-funded by the European Regional Development Fund (FEDER)},
publisher = {Nature Publishing Group},
title = {T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus},
doi = {10.1038/s41597-023-02737-4},
author = {Rodríguez León, Ciro and Avilés-Pérez, M. D. and Baños Legrán, Oresti and Quesada Charneco, Miguel and Lopez-Ibarra Lozano, Pablo J. and Villalonga Palliser, Claudia and Muñoz Torres, Manuel Eduardo},
}