Glucostats: an efficient Python library for glucose time series feature extraction and visual analysis
GlucoStats demonstrates high efficiency in processing large-scale medical datasets in minimal time. Its modular design enables easy customization and extension, making it adaptable to diverse research and clinical needs. By offering precise CGM data analysis and user-friendly visualization tools, it serves both technical researchers and non-technical users, such as physicians and patients, with practical and research-driven applications.
Authors
- David Chushig-Muzo (ORCID: https://orcid.org/0000-0001-5585-2305)
- Ana M. Wägner (ORCID: https://orcid.org/0000-0002-7663-9308)
- Conceição Granja (ORCID: https://orcid.org/0000-0002-3028-8899)
- Cristina Soguero-Ruíz (ORCID: https://orcid.org/0000-0001-5817-989X)
- Francisco J. Lara-Abelenda (ORCID: https://orcid.org/0000-0001-5565-8203)
- Pablo Peiro-Corbacho
Institutions
- Universidad de Las Palmas de Gran Canaria (ES)
- University Hospital of North Norway (NO)
- Universidad Rey Juan Carlos (ES)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1186/s12859-025-06250-w
- Primary Topic
- Diabetes Management and Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Horizon 2020 Framework Programme
- Agencia Estatal de Investigación