Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling
Abstract Background Type 1 diabetes (T1D) is a chronic endocrine disorder characterized by high blood glucose levels, impacting millions of people globally. Its management requires intensive insulin therapy, frequent blood glucose monitoring, and lifestyle adjustments. The accurate prediction of the...
Saved in:
Main Authors: | Daphne N. Katsarou, Eleni I. Georga, Maria A. Christou, Panagiota A. Christou, Stelios Tigas, Costas Papaloukas, Dimitrios I. Fotiadis |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2025-01-01
|
Series: | BMC Medical Informatics and Decision Making |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12911-025-02867-2 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes
by: Fernando Sebastian‐Valles, et al.
Published: (2025-01-01) -
Metabolic syndrome in type 1 diabetes: higher time above range and glycemic variability revealed by continuous glucose monitoring (CGM)
by: Yayu Fang, et al.
Published: (2025-02-01) -
Towards a decision support system for post bariatric hypoglycaemia: development of forecasting algorithms in unrestricted daily-life conditions
by: Francesco Prendin, et al.
Published: (2025-01-01) -
Analytical Accuracy of a Continuous Glucose Monitor in Adult Diabetic KetoacidosisTake-Home Points
by: Nathan L. Haas, MD, et al.
Published: (2025-03-01) -
Positive association between the proinsulin-to-C-peptide ratio and prolonged hyperglycemic time in type 2 diabetes
by: Aika Miya, et al.
Published: (2024-04-01)