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We introduce GlyTwin as a novel digital twin framework designed to support individuals with Type 1 Diabetes through personalized and actionable behavioral interventions. Rather than only predicting glucose outcomes, GlyTwin generates counterfactual treatment recommendations that suggest minimal changes in insulin dosing, carbohydrate intake, and insulin timing to help prevent hyperglycemia.

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.