5 papers
GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes
Davide Marelli, Giorgia Rigamonti, Mirko Paolo Barbato +1
Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes management, particularly for type 1 diabetes. Th…
Subject-Conditioned Glucose Forecasting in Type-1 Diabetes
Giorgia Rigamonti, Mirko Paolo Barbato, Davide Marelli +1
Accurate forecasting of blood glucose concentration is key in the management of Type 1 Diabetes, facilitating early detection of adverse glycemic events and supporting timely thera…
Tailoring Adverse Event Prediction in Type 1 Diabetes with Patient-Specific Deep Learning Models
Giorgia Rigamonti, Mirko Paolo Barbato, Davide Marelli +1
Effective management of Type 1 Diabetes requires continuous glucose monitoring and precise insulin adjustments to prevent hyperglycemia and hypoglycemia. With the growing adoption…
The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Prediction
Andrea Corsico, Giorgia Rigamonti, Simone Zini +2
In this work, we present a network-specific approach for predicting brain responses to complex multimodal movies, leveraging the Yeo 7-network parcellation of the Schaefer atlas. R…
Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes
Mirko Paolo Barbato, Giorgia Rigamonti, Davide Marelli +1
Type 1 Diabetes (T1D) affects millions worldwide, requiring continuous monitoring to prevent severe hypo- and hyperglycemic events. While continuous glucose monitoring has improved…