Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study.

ABSTRACT:

Background

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.

Methods

We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was us

SUBMITTER: van Doorn WPTM 

PROVIDER: S-EPMC8224858 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature