Maria Adela Grando

Saman Khamesian, Asiful Arefeen, Maria Adela Grando et al.

Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels to avoid the harmful effects of dysglycemia, including both hyperglycemia and hypoglycemia. Despite the development of advanced technologies such as automated insulin delivery (AID) systems, achieving optimal glycemic control remains challenging. AID systems combine continuous subcutaneous insulin infusion with data from continuous glucose monitors (CGMs), offering potential benefits in reducing glucose variability and increasing time-in-range. However, these systems still frequently fail to prevent dysglycemia, partly due to limitations in their prediction algorithms, which lack the accuracy needed to avert abnormal glucose events. This shortcoming highlights the need for more advanced glucose forecasting methods. To address this need, we introduce GLIMMER, Glucose Level Indicator Model with Modified Error Rate, a machine learning-based model for predicting blood glucose levels. GLIMMER classifies glucose values into normal and abnormal ranges and employs a novel custom loss function that prioritizes accuracy in dysglycemic regions, where patient safety is most critical. To evaluate GLIMMER's effectiveness for T1D management, we used both a publicly available dataset and a newly collected dataset involving 25 individuals with T1D. In forecasting glucose levels for the next hour, GLIMMER achieved a root mean square error (RMSE) of 23.97 (+/-3.77) and a mean absolute error (MAE) of 15.83 (+/-2.09) mg/dL. These results represent a 23% improvement in RMSE and a 31% improvement in MAE compared to the best previously reported models.

Saman Khamesian, Asiful Arefeen, Bithika M. Thompson et al.

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.

Asiful Arefeen, Saman Khamesian, Maria Adela Grando et al.

Frequent and long-term exposure to hyperglycemia (i.e., high blood glucose) increases the risk of chronic complications such as neuropathy, nephropathy, and cardiovascular disease. Current technologies like continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) primarily model specific aspects of glycemic control-like hypoglycemia prediction or insulin delivery. Similarly, most digital twin approaches in diabetes management simulate only physiological processes. These systems lack the ability to offer alternative treatment scenarios that support proactive behavioral interventions. To address this, we propose GlyTwin, a novel digital twin framework that uses counterfactual explanations to simulate optimal treatments for glucose regulation. Our approach helps patients and caregivers modify behaviors like carbohydrate intake and insulin dosing to avoid abnormal glucose events. GlyTwin generates behavioral treatment suggestions that proactively prevent hyperglycemia by recommending small adjustments to daily choices, reducing both frequency and duration of these events. Additionally, it incorporates stakeholder preferences into the intervention design, making recommendations patient-centric and tailored. We evaluate GlyTwin on AZT1D, a newly constructed dataset with longitudinal data from 21 type 1 diabetes (T1D) patients on automated insulin delivery systems over 26 days. Results show GlyTwin outperforms state-of-the-art counterfactual methods, generating 76.6% valid and 86% effective interventions. These findings demonstrate the promise of counterfactual-driven digital twins in delivering personalized healthcare.