Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation.

ABSTRACT:

Background

Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance.

Methods

To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties. On the task of BG forecasting, we test how well our method brings performance closer to th

SUBMITTER: Gomez LA 

PROVIDER: S-EPMC11688677 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature