Modeling strategies for CGM data: A scoping review of mechanistic, machine learning, and hybrid approaches in diabetes management
Challenges such as data heterogeneity, the limited availability of high-quality datasets beyond T1DM, and reduced cross-cohort generalizability persist, underscoring the need for standardized validation procedures and physiologically informed modeling strategies.
Authors
- Antonio Cutruzzolà (ORCID: https://orcid.org/0000-0002-6848-4262)
- Carlo Cosentino (ORCID: https://orcid.org/0000-0001-5768-1829)
- Paolo Zaffino (ORCID: https://orcid.org/0000-0002-0219-0157)
- Anna Procopio (ORCID: https://orcid.org/0000-0002-3639-8714)
- Nilde Fera
- Concetta Irace
Institutions
- Magna Graecia University (IT)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2026-03-03
- DOI
- https://doi.org/10.1016/j.compbiomed.2026.111601
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00