MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention
Researchers have developed MetaPlate, a novel framework designed to provide personalized meal recommendations for managing postprandial hyperglycemia. This system integrates continuous glucose monitoring (CGM) data, physiological signals from wearables, and user meal inputs from 25 individuals. It utilizes a machine learning model to predict glucose response and a counterfactual optimization module to adjust meal composition, aiming to keep glucose levels below 140 mg/dL. An LLM-based retrieval-augmented generation (RAG) layer then translates these adjustments into human-readable dietary advice, which was refined through expert assessments with registered dietitians. AI
IMPACT This system demonstrates a novel application of LLMs and machine learning for personalized health management, potentially improving dietary adherence and metabolic health outcomes.