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Proposed a novel framework for generating CFs using finetuned large language models (LLMs), with a focus on structured sensor-derived datasets in health and physiological monitoring

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.