Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring
Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts.
Authors
- Morten Hasselstrøm Jensen (ORCID: https://orcid.org/0000-0002-6649-8644)
- Søren Schou Olesen (ORCID: https://orcid.org/0000-0003-3916-3168)
- Simon Lebech Cichosz (ORCID: https://orcid.org/0000-0002-3484-7571)
Institutions
- Novo Nordisk (Denmark) (DK)
- Aalborg University Hospital (DK)
- Mech-Sense (DK)
- Aalborg University (DK)
Publication Details
- Journal
- Journal of Diabetes Science and Technology
- Published
- 2024-10-08
- DOI
- https://doi.org/10.1177/19322968241286907
- Citations
- 14
- Primary Topic
- Diabetes Management and Research
- Type
- article
- Field-Weighted Citation Impact
- 5.01