AZT1D: A Real-World Dataset for Type 1 Diabetes

Saman Khamesian, Asiful Arefeen, Bithika M. Thompson, Maria Adela Grando, Hassan Ghasemzadeh

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Keywords: e-health, wearable sensors, type 1 diabetes, continuous glucose monitoring, automated insulin delivery systems, machine learning

Abstract: 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.

Track: 3. Signal processing, machine learning, deep learning, and decision-support algorithms for digital and computational health

NominateReviewer: Asiful Arefeen: [e-mail], Hassan Ghasemzadeh: [e-mail]

Submission Number: 11