GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting

arXiv:2601.05353 · cs.LG, cs.IT, math.IT · Submitted 2026-01-08 · Read on arXiv

cs.LG, cs.IT, math.IT

Submitted: 2026-01-08

Updated: 2026-09-23

Terminology

• RAG — 38× in this paper · explained in 3 episodes
• Prediction Horizon — 17× in this paper
• Accuracy — 14× in this paper · explained in 1 episode
• Transformer — 14× in this paper · explained in 2 episodes
• Clarke Error Grid — 12× in this paper
• Embeddings — 12× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Trace — 5× in this paper · explained in 2 episodes
• Arizona State University — 4× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Mean Absolute Error — 2× in this paper
• Root Mean Square Error — 2× in this paper
• AND E RRONEOUS P REDICTION — 1× in this paper
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• B ENIGN E RROR — 1× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decision-Support Tool — 1× in this paper · explained in 2 episodes
• Digital Twin — 1× in this paper · explained in 4 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• LLM for Time-Series Forecasting — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• M ETRICS REPORT ACCURATE P REDICTION — 1× in this paper
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• National Science Foundation — 1× in this paper
• Personalization — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Time In Range — 1× in this paper
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals
• Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting
• Large Language Models are Few-Shot Health Learners
• Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statistics
• Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes

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