AZT1D提供25名1型糖尿病患者的真实世界数据，助力个性化治疗研究。
AZT1D: A Real-World Dataset for Type 1 Diabetes
- 收录25名患者6-8周的连续血糖、胰岛素泵及碳水摄入等多模态数据
- 包含罕见的精准胰岛素剂量细节，如校正量与餐前注射类型
- 适合做糖尿病智能管理、数字孪生和预测模型的研究者使用
高质量的真实世界数据对推进1型糖尿病（T1D）管理中的数据驱动方法至关重要，包括个性化治疗设计、数字孪生系统和葡萄糖预测模型。然而，该领域进展受限于公开可获取且数据详尽的患者数据集稀缺。为填补这一空白，我们提出了AZT1D数据集，包含25名使用自动胰岛素输注（AID）系统的T1D患者数据。该数据集涵盖每位患者持续6至8周的连续血糖监测（CGM）、胰岛素泵与胰岛素给药记录、碳水化合物摄入量及设备模式（常规、睡眠、运动）信息。特别地，数据集提供了极为细致的胰岛素冲量（bolus）信息，包括总剂量、类型及校正剂量等，这些特征在现有数据集中极为少见。通过提供丰富且自然状态下的数据，AZT1D支持多种人工智能与机器学习应用，旨在提升临床决策能力和个体化护理水平。
原文摘要 · Abstract (English)
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 to 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.
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