This work focuses on improving blood glucose prediction by incorporating glycemic-aware training strategies that better capture hypo- and hyperglycemic events.
We propose Artificial Intelligence for Modeling and Explaining Neonatal Health (AIMEN), a deep learning framework that predicts adverse labor outcomes from risk factors and provides the model's reasoning.
An innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson's Disease (PD) patients, using a single triaxial accelerometer