GluMarker: A Novel Predictive Modeling of Glycemic Control Through Digital Biomarkers

TL;DR

GluMarker is an innovative framework that utilizes digital biomarkers and machine learning to predict overall glycemic control, providing new insights for diabetes management beyond insulin dosing.

Contribution

The paper introduces GluMarker, a comprehensive machine learning-based framework that broadens digital biomarker application for predicting glycemic control in diabetes.

Findings

Achieved state-of-the-art prediction accuracy on Anderson's dataset.

Identified key digital biomarkers influencing next-day glycemic control.

Enhanced understanding of daily factors affecting diabetes management.

Abstract

The escalating prevalence of diabetes globally underscores the need for diabetes management. Recent research highlights the growing focus on digital biomarkers in diabetes management, with innovations in computational frameworks and noninvasive monitoring techniques using personalized glucose metrics. However, they predominantly focus on insulin dosing and specific glucose values, or with limited attention given to overall glycemic control. This leaves a gap in expanding the scope of digital biomarkers for overall glycemic control in diabetes management. To address such a research gap, we propose GluMarker -- an end-to-end framework for modeling digital biomarkers using broader factors sources to predict glycemic control. Through the assessment and refinement of various machine learning baselines, GluMarker achieves state-of-the-art on Anderson's dataset in predicting next-day glycemic control. Moreover, our research identifies key digital biomarkers for the next day's glycemic control prediction. These identified biomarkers are instrumental in illuminating the daily factors that influence glycemic management, offering vital insights for diabetes care.