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.

翻译：全球糖尿病患病率的持续上升凸显了糖尿病管理的必要性。近期研究强调了数字生物标志物在糖尿病管理中日益增长的应用，包括利用个性化血糖指标的计算框架创新和非侵入性监测技术。然而，现有研究主要集中于胰岛素剂量和特定血糖值，或对整体血糖控制的关注有限，这导致在拓展糖尿病管理中整体血糖控制数字生物标志物的范围方面存在空白。为填补这一研究空白，我们提出GluMarker——一种利用更广泛因素来源对数字生物标志物进行建模以预测血糖控制的端到端框架。通过对多种机器学习基线模型的评估与优化，GluMarker在Anderson数据集上实现了次日血糖控制预测的最优性能。此外，我们的研究识别出预测次日血糖控制的关键数字生物标志物。这些被识别的生物标志物有助于阐明影响血糖管理的日常因素，为糖尿病护理提供重要洞察。