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.
A hybrid attention framework designed for accurate and efficient blood glucose forecasting using multimodal data using feature decomposition and knowledge distillation