Toward Short-Term Glucose Prediction Solely Based on CGM Time Series
This addresses real-time glucose prediction for diabetic patients, offering a privacy-preserving solution, though it is incremental as it builds on existing methods with a focus on data constraints.
The paper tackles short-term glucose prediction for diabetes management by proposing TimeGlu, an end-to-end pipeline using only CGM time series data, achieving state-of-the-art performance on two datasets without requiring additional personal data.
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.