AZT1D: A Real-World Dataset for Type 1 Diabetes

High-quality real-world datasets are essential for advancing data-driven approaches in type 1 diabetes (T1D) management, including personalized therapy design, digital twin systems, and glucose prediction models. However, progress in this area has been limited by the scarcity of publicly available datasets that offer detailed and comprehensive patient data. To address this gap, we present AZT1D, a dataset containing data collected from 25 individuals with T1D on automated insulin delivery (AID) systems. AZT1D includes continuous glucose monitoring (CGM) data, insulin pump and insulin administration data, carbohydrate intake, and device mode (regular, sleep, and exercise) obtained over 6–8 weeks for each patient. Notably, the dataset provides granular details on bolus insulin delivery (i.e., total dose, bolus type, correction-specific amounts) features that are rarely found in existing datasets. By offering rich, naturalistic data, AZT1D supports a wide range of artificial intelligence and machine learning applications aimed at improving clinical decision-making and individualized care in T1D.高质量的真实世界数据集对于推动1型糖尿病（type 1 diabetes, T1D）管理领域的数据驱动方法发展至关重要，此类应用涵盖个性化治疗方案设计、数字孪生系统以及血糖预测模型等方向。然而，当前该领域的发展受限于可公开获取且包含详尽全面患者数据的数据集稀缺问题。为填补这一空白，我们推出AZT1D数据集，该数据集收录了25名使用自动胰岛素输送（automated insulin delivery, AID）系统的1型糖尿病患者的相关数据。AZT1D涵盖每位患者6至8周内采集的连续血糖监测（continuous glucose monitoring, CGM）数据、胰岛素泵与胰岛素给药数据、碳水化合物摄入情况，以及设备工作模式（常规、睡眠、运动模式）。值得注意的是，该数据集提供了现有数据集鲜有收录的大剂量胰岛素输注（bolus insulin delivery）相关精细特征细节，包括总给药剂量、大剂量类型、校正专用给药量等。凭借丰富且贴合真实临床场景的自然数据，AZT1D可支撑诸多人工智能与机器学习应用，助力优化1型糖尿病管理中的临床决策与个体化诊疗。