Functional data analysis and prediction tools for continuous glucose-monitoring studies.

ABSTRACT:

Introduction

To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes.

Methods

A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and wit

SUBMITTER: Gecili E 

PROVIDER: S-EPMC8057494 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature