We introduce NutriGen, a framework based on large language models (LLM) designed to generate personalized meal plans that align with user-defined dietary preferences and constraints.
LEAD is an explainable AI technique that determines relative feature contributions by characterizing the decision boundary and perturbing critical samples along the decision boundary close to the test sample. LEAD achieves at least 6% improved fidelity and 7% improved consistency compared to LIME and SHAP.
MealMeter is a linear regression based technique applied on multi-modal data collected using a CGM sensor and a wristband and tracks meal macronutrients. MealMeter achieves as low as 0.37 average root mean squared relative errors (RMSRE) in carb tracking, which is at least 15.9% improvement compared to TabPFN foundational model and other baselines.