Continuous Glucose Monitoring

Follow this topic Ask about this topic

Latest papers

16 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Reliability-Aware Short-Term Glycemic Risk Prediction Using an Anchor-Based Comparative Framework of Sequence, Multimodal, and State-Updating Digital-Twin Models
Oct 26, 2026Pir Bakhsh Khokhar, Carmine Gravino, Fabio PalombaDigital Twins Multimodal Model Evaluation
Department of Computer Science, University of Salerno, Fisciano, Italy
GlucoPRISM: Protocol-Supervised Pretraining for Cross-Cohort Glucose Transfer
Oct 8, 2026Adinath Madhavrao Dukre, Abdullah Mohammad, Yifan Lu +3Continuous Glucose Monitoring Cross-Domain Transfer Learning
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Vitalverse, Abu Dhabi, UAE · Google, USA
RACE: Residual-Aware Test-Time Adaptation for Neighbor-Rich Time-Series Foundation Model Forecasting
Sep 30, 2026Hao-Nan Shi, Tong Wu, Chen-Cong Sun +3Residual Learning Time Series Forecasting
School of Artificial Intelligence, Nanjing University · National Key Laboratory for Novel Software Technology, Nanjing University · Nanjing, China
Transferable Evidence Reconstruction for Longitudinal Glucose Representations
Sep 23, 2026Tian Zhou, Bingqing Peng, Linxiao Yang +6Representation Learning Self-Supervised Time Series Representation Learning
Ant Group · Independent Researcher
Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
Sep 10, 2026Bowen Zhang, Hsiu-Wen Cheng, Hongyu Yang +9Zero-Shot Time Series Forecasting Blood Glucose Forecasting
University of California, Los Angeles, Los Angeles, CA, U.S.A. · Union County Magnet High School, Scotch Plain, NJ, U.S.A. · Huntington Beach High School, Huntington Beach, CA, U.S.A. +3
FairGlucose: A CGM Fairness Benchmark Reveals Subgroup Disparities Hidden in Population-Level Validation
Aug 18, 2026Junjie Luo, Xuzhe Zhi, Rui Han +5Continuous Glucose Monitoring Algorithmic Fairness
Carey Business School Johns Hopkins University Baltimore, MD, USA · Welldoc, Inc. Columbia, MD, USA
MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes
Jun 17, 2026Nathaniel Jeffries, Miriam Wolff, Sam Royston +5Multivariate Time Series Forecasting Type 1 Diabetes
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA · Replica Health, New York, NY, USA · Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA +1
An Exploratory Study of Blood Glucose Estimation from Photoplethysmography Signals using Machine Learning
Jun 14, 2026Ruhani Bhatia, Vijval EkboteWearable Sensing Photoplethysmography
Indraprastha Institute of Information Technology, Delhi
GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring
May 29, 2026Zechen Li, Keerthana Natarajan, Weizhi Zhang +11Irregular Time-Series Modeling Latent Dynamics Modeling
Google Research · Work done during an internship at Google · University of New South Wales, Sydney
CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
May 1, 2026Hada Melino Muhammad, Zechen Li, Flora Salim +1Cross-Modal Representation Learning Self-Supervised Time Series Representation Learning
University of New South Wales · Google Research
If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data
Apr 18, 2026Yanjun Cui, Ali Emami, Temiloluwa Prioleau +1Privacy-Preserving Language Models Healthcare
Dartmouth College · Emory University
Blinded Multi-Rater Comparative Evaluation of a Large Language Model and Clinician-Authored Responses in CGM-Informed Diabetes Counseling
Apr 16, 2026Zhijun Guo, Alvina Lai, Emmanouil Korakas +6Clinical Decision Support RAG Evaluation
Institute of Health Informatics, University College London, London, United Kingdom · University College London Hospitals NHS Foundation Trust, London, United Kingdom · Dartford and Gravesham NHS Foundation Trust, Dartford, United Kingdom +1
A unified data format for managing diabetes time-series data: DIAbetes eXchange (DIAX)
Apr 13, 2026Elliott C. Pryor, Marc D. Breton, Anas El FathiContinuous Glucose Monitoring Healthcare
University of Virginia, Center for Diabetes Technology
The Stationarity Bias: Stratified Stress-Testing for Time-Series Imputation in Regulated Dynamical Systems
Feb 17, 2026Amirreza Dolatpour Fathkouhi, Alireza Namazi, Heman ShakeriContinuous Glucose Monitoring Time Series Imputation
Department of Computer Science Center for Diabetes Technology University of Virginia Charlottesville, Virginia, USA · School of Data Science Center for Diabetes Technology University of Virginia Charlottesville, Virginia, USA
GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting
Jan 8, 2026Shovito Barua Soumma, Hassan GhasemzadehTime Series Forecasting Blood Glucose Forecasting
College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA
Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutions
Jul 18, 2025Temiloluwa Prioleau, Baiying Lu, Yanjun CuiForecasting Benchmarks Blood Glucose Forecasting
Department of Computer Science Emory University Atlanta, GA 30322 · Department of Computer Science Dartmouth College Hanover, NH 03755

Oct 26, 2026J/K move · Enter open · S save

Reliability-Aware Short-Term Glycemic Risk Prediction Using an Anchor-Based Comparative Framework of Sequence, Multimodal, and State-Updating Digital-Twin Models

Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba

Department of Computer Science, University of Salerno, Fisciano, Italy

Digital Twins Multimodal Model Evaluation Continuous Glucose Monitoring Multimodal Model Interpretability

Open paper AskPDF arXiv

Anticipating short-term hypoglycemic and hyperglycemic risk is essential for proactive diabetes management. However, existing machine learning approaches often use inconsistent evaluation protocols and emphasize predictive performance over reliability. This study introduces an anchor-based benchmarking framework integrating longitudinal glucose sequences and multimodal patient information. Using the T1DiabetesGranada dataset, 5,503,441 standardized prediction anchors were constructed from 736 individuals with type 1 diabetes to evaluate hypoglycemia and hyperglycemia prediction at 30-, 60-, and 120-minute horizons. Three neural architectures, a Hybrid LSTM-Transformer, a Multimodal Digital Twin, and a State-Updating Digital Twin with gap-aware longitudinal state progression, were compared under identical patient-level validation protocols. Beyond predictive performance, the evaluation examined calibration, classification thresholds, explanation reliability, cross-model disagreement, and robustness to controlled perturbations. No statistically significant predictive differences were observed after Holm correction, although the models exhibited complementary reliability characteristics. The Multimodal Digital Twin achieved the lowest mean Brier score, while the State-Updating Digital Twin demonstrated the lowest mean expected calibration error (ECE-10). The Hybrid LSTM-Transformer showed strong explanation faithfulness and stability under its temporal attribution protocol. These findings highlight the importance of evaluating digital-twin models beyond predictive accuracy and emphasize the need for reliability assessment and external validation before clinical deployment.