A Study on Personalized Blood Glucose Forecasting and Segmentation using Domain-Specific Variables

Abstract

This study focuses on the importance of short-term glucose prediction in diabetes management facilitated by Continuous Glucose Monitoring (CGM). It recognizes the pivotal role of variables such as diet and insulin intake in glycemic control, while also acknowledging the challenges in effectively collecting this information. To address the gaps presented by missing data, the research prioritizes the identification and application of surrogate markers. These markers indirectly reflect the impact of dietary and physical activity on blood glucose levels, thereby enhancing the accuracy of short-term predictions and contributing to personalized diabetes management. The proposed short-term prediction method incorporates exogenous variables, such as Time in Range (TIR), Continuous Overlapping Net Glycemic Action (CONGA), Mean Amplitude of Glycemic Excursion (MAGE), and Coefficient of Variation (CV). This study presents the framework for glucose prediction and self-management in the context of missing data. This approach enhances the accuracy of short-term predictions and contributes to personalized diabetes management.