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

• URL: http://arxiv.org/abs/2601.05353v1
• Date: Thu, 08 Jan 2026 20:07:59 GMT
• Title: GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting
• Authors: Shovito Barua Soumma, Hassan Ghasemzadeh,
• Abstract summary: GlyRAG is a context-aware, retrieval-augmented forecasting framework that derives semantic understanding of blood glucose dynamics directly from CGM traces.<n>GlyRAG consistently outperforms state-of-the art methods, achieving up to 39% lower RMSE and a further 1.7% reduction in RMSE over the baseline.
• Score: 3.494950334697973
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate forecasting of blood glucose from CGM is essential for preventing dysglycemic events, thus enabling proactive diabetes management. However, current forecasting models treat blood glucose readings captured using CGMs as a numerical sequence, either ignoring context or relying on additional sensors/modalities that are difficult to collect and deploy at scale. Recently, LLMs have shown promise for time-series forecasting tasks, yet their role as agentic context extractors in diabetes care remains largely unexplored. To address these limitations, we propose GlyRAG, a context-aware, retrieval-augmented forecasting framework that derives semantic understanding of blood glucose dynamics directly from CGM traces without requiring additional sensor modalities. GlyRAG employs an LLM as a contextualization agent to generate clinical summaries. These summaries are embedded by a language model and fused with patch-based glucose representations in a multimodal transformer architecture with a cross translation loss aligining textual and physiological embeddings. A retrieval module then identifies similar historical episodes in the learned embedding space and uses cross-attention to integrate these case-based analogues prior to making a forecasting inference. Extensive evaluations on two T1D cohorts show that GlyRAG consistently outperforms state-of-the art methods, achieving up to 39% lower RMSE and a further 1.7% reduction in RMSE over the baseline. Clinical evaluation shows that GlyRAG places 85% predictions in safe zones and achieves 51% improvement in predicting dysglycemic events across both cohorts. These results indicate that LLM-based contextualization and retrieval over CGM traces can enhance the accuracy and clinical reliability of long-horizon glucose forecasting without the need for extra sensors, thus supporting future agentic decision-support tools for diabetes management.

Related papers

• Integrating Neural Differential Forecasting with Safe Reinforcement Learning for Blood Glucose Regulation [51.12307713554633]
  TSODE is a safety-aware controller that integrates Thompson RL Sampling with a Neural Ordinary Differential Equation forecaster.<n>In the FDA-approved UVa/Padova simulator (adult cohort), TSODE achieved 87.9% time-in-range with less than 10% time below 70 mg/dL.
  arXiv  Detail & Related papers  (2025-11-16T02:11:33Z)
• SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management [0.6481500397175589]
  Continuous glucose monitoring (CGM) generates dense data streams critical for diabetes management.<n>We present SSM-CGM, a Mamba-based neural state-space forecasting model that integrates CGM and wearable activity signals.
  arXiv  Detail & Related papers  (2025-10-05T22:37:28Z)
• LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR [51.11296719862485]
  We propose a Contrastive Learning and Cross-Modal alignment framework for gadolinium-free cardiomyopathy screening using cine CMR sequences.<n>By aligning the latent spaces of cine CMR and Late Gadolinium Enhancement (LGE) sequences, our model encodes fibrosis-specific pathology into cine CMR embeddings.
  arXiv  Detail & Related papers  (2025-08-23T07:21:23Z)
• AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on AI-READI Dataset [8.063401183752347]
  Diabetes is a chronic metabolic disorder characterized by persistently high blood glucose levels (BGLs)<n>Recent deep learning models show promise in improving BGL prediction.<n>We propose AttenGluco, a multimodal Transformer-based framework for long-term blood glucose prediction.
  arXiv  Detail & Related papers  (2025-02-14T05:07:38Z)
• Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting [6.466206145151128]
  GlucoNet is an AI-powered sensor system for continuously monitoring behavioral and physiological health.<n>We propose a decomposition-based transformer model that incorporates patients' behavioral and physiological data.<n>G GlucoNet achieves a 60% improvement in RMSE and a 21% reduction in the number of parameters, improving RMSE and MAE by 51% and 57%, using data obtained involving 12 participants with T1-Diabetes.
  arXiv  Detail & Related papers  (2024-11-16T05:09:20Z)
• GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks [0.12564343689544843]
  Continuous glucose monitors (CGM) are small medical devices that measure blood glucose levels at regular intervals. Forecasting of glucose trajectories based on CGM data holds the potential to substantially improve diabetes management.
  arXiv  Detail & Related papers  (2024-10-08T08:01:09Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning.<n>Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions [4.073768455373616]
  Continuous glucose monitoring (CGM) devices provide real-time glucose monitoring and timely alerts for glycemic excursions. Rare events like hypoglycemia and hyperglycemia remain challenging due to their infrequency. We propose a novel Hypo-Hyper (HH) loss function, which significantly improves performance in the glycemic excursion regions.
  arXiv  Detail & Related papers  (2024-08-25T19:51:27Z)
• From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [47.23780364438969]
  We present GluFormer, a generative foundation model for CGM data that learns nuanced glycemic patterns and translates them into predictive representations of metabolic health.<n>GluFormer generalizes to 19 external cohorts spanning different ethnicities and ages, 5 countries, 8 CGM devices, and diverse pathophysiological states.<n>In a longitudinal study of 580 adults with CGM data and 12-year follow-up, GluFormer identifies individuals at elevated risk of developing diabetes more effectively than blood HbA1C%.
  arXiv  Detail & Related papers  (2024-08-20T13:19:06Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.