Toward Short-Term Glucose Prediction Solely Based on CGM Time Series

TL;DR

This paper introduces TimeGlu, a novel end-to-end model for short-term glucose prediction using only CGM time series data, enabling real-time diabetes management without privacy concerns.

Contribution

The paper presents a new model that predicts glucose levels solely from CGM data, outperforming existing methods and avoiding the need for additional physiological information.

Findings

TimeGlu achieves state-of-the-art prediction accuracy.

The model performs well across two different datasets.

It provides effective real-time guidance for diabetes management.

Abstract

The global diabetes epidemic highlights the importance of maintaining good glycemic control. Glucose prediction is a fundamental aspect of diabetes management, facilitating real-time decision-making. Recent research has introduced models focusing on long-term glucose trend prediction, which are unsuitable for real-time decision-making and result in delayed responses. Conversely, models designed to respond to immediate glucose level changes cannot analyze glucose variability comprehensively. Moreover, contemporary research generally integrates various physiological parameters (e.g. insulin doses, food intake, etc.), which inevitably raises data privacy concerns. To bridge such a research gap, we propose TimeGlu -- an end-to-end pipeline for short-term glucose prediction solely based on CGM time series data. We implement four baseline methods to conduct a comprehensive comparative analysis of the model's performance. Through extensive experiments on two contrasting datasets (CGM Glucose and Colas dataset), TimeGlu achieves state-of-the-art performance without the need for additional personal data from patients, providing effective guidance for real-world diabetic glucose management.