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 short-term course of glucose levels in the subcutaneous space in T1D people, as measured by a continuous glucose monitoring (CGM) system, is essential for improving glucose control by avoiding harmful hypoglycaemic and hyperglycaemic glucose swings, facilitating precise insulin management and individualized care and, in turn, minimizing long-term vascular complications.

Methods

In this study, we propose an ensemble univariate short-term predictive model of the subcutaneous glucose concent

SUBMITTER: Katsarou DN 

PROVIDER: S-EPMC11783934 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature