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

arXiv: https://arxiv.org/abs/2404.12605 · HTML: https://mlsanity.com/paper/2404.12605/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Cross-Entropy Loss Functions: Theoretical Analysis and Applications](https://mlsanity.com/paper/2304.07288/) (2023)
- [DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization](https://mlsanity.com/paper/2010.00573/) (2020)
- [Bootstrapping Vision-Language Learning with Decoupled Language Pre-training](https://mlsanity.com/paper/2307.07063/) (2023)
- [FT2TF: First-Person Statement Text-To-Talking Face Generation](https://mlsanity.com/paper/2312.05430/) (2023)
- [GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs](https://mlsanity.com/paper/2210.15578/) (2022)
- [Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection](https://mlsanity.com/paper/2402.10862/) (2024)
- [Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models](https://mlsanity.com/paper/2306.03799/) (2023)
- [AV-MaskEnhancer: Enhancing Video Representations through Audio-Visual Masked Autoencoder](https://mlsanity.com/paper/2309.08738/) (2023)
- [Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People](https://mlsanity.com/paper/2006.15940/) (2020)
- [SAIC: Integration of Speech Anonymization and Identity Classification](https://mlsanity.com/paper/2312.15190/) (2023)
- [CrossGP: Cross-Day Glucose Prediction Excluding Physiological Information](https://mlsanity.com/paper/2404.10901/) (2024)
- [Toward Short-Term Glucose Prediction Solely Based on CGM Time Series](https://mlsanity.com/paper/2404.11924/) (2024)

## Built on by (most cited first, 2 of 2)

- [GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals](https://mlsanity.com/paper/2504.09846/) (2025)
- [Learning Sparsity for Effective and Efficient Music Performance Question Answering](https://mlsanity.com/paper/2506.01319/) (2025)

## Abstract (arXiv)

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.