Digital biomarkers can radically transform the standard of care for chronic conditions that are complex to manage. In this work, we propose a scalable computational framework for discovering digital biomarkers of glycemic control. As a feasibility study, we leveraged over 79,000 days of digital data to define objective features, model the impact of each feature, classify glycemic control, and identify the most impactful digital biomarkers. Our research shows that glycemic control varies by age group, and was worse in the youngest population of subjects between the ages of 2–14. In addition, digital biomarkers like prior-day time above range and prior-day time in range, as well as total daily bolus and total daily basal were most predictive of impending glycemic control. With a combination of the top-ranked digital biomarkers, we achieved an average F1 score of 82.4% and 89.7% for classifying next-day glycemic control across two unique datasets.

数字生物标志物可以从根本上改变管理复杂的慢性病的护理标准。在这项工作中，我们提出了一个可扩展的计算框架，用于发现血糖控制的数字生物标志物。作为一项可行性研究，我们利用超过 79,000 天的数字数据来定义客观特征，模拟每个特征的影响，对血糖控制进行分类，并确定最具影响力的数字生物标志物。我们的研究表明，血糖控制因年龄组而异，并且在 2-14 岁的最年轻受试者群体中更差。此外，数字生物标志物，如前一天时间高于范围和前一天时间范围内，以及每日总推注量和每日总基础时间，最能预测即将发生的血糖控制。结合顶级数字生物标志物，