Blood Glucose Forecasting

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Blood glucose forecasting estimates how glucose will change over minutes to hours, helping people with diabetes and clinicians anticipate highs, lows, and treatment needs. Methods range from LSTMs, temporal convolutional networks, and transformers trained on CGM histories to personalized models that incorporate insulin, meals, wearable signals, or physiological models. Researchers also test foundation models, retrieval, and uncertainty estimates, using benchmarks that compare forecast errors across datasets, prediction horizons, and patient groups. A central challenge is that strong average accuracy does not ensure reliable warnings, fair performance across subgroups, or correct predictions of responses to planned interventions.

Latest papers

20 papers3 papers in the last four weeks, down 25% on the four weeks before. 0.0% of all new papers.

Evaluating Type-1 Diabetes Digital Twins by Decision Quality
Oct 22, 2026Victor Li, Owen Tucker, Michael S. Hughes +2Digital Twins Clinical Decision-Making
Department of Computer Science, Emory University · School of Medicine, Emory University
SSMCGM-Stream: A Streaming State-Space Model for Personalized Diabetes Management
Oct 20, 2026Shakson Isaac, Chirag PatelBlood Glucose Forecasting Explainable Time Series Forecasting
Department of Biomedical Informatics, Harvard Medical School
PMB-NN: Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes
Oct 11, 2026Yaowen Zhang, Shuhao Que, Ying WangBlood Glucose Forecasting Type 1 Diabetes
Department of Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
Using Machine Learning to Investigate Predictors of Fasting Blood Glucose: Insights into Circadian Timing and Age Interactions
Sep 26, 2026Viktoriya Bu-Dager, Silvia CirsteaBlood Glucose Forecasting Clinical Prediction
School of Computing and Information Science, Anglia Ruskin University East Road, Cambridge CB1 1PT, U.K.
Predicting Postprandial Glycemic Response from Meal Images, Clinical Variables, and Gut Microbiome Information
Sep 21, 2026Varvara Kondratyeva, Kamilia Zaripova, Nassir Navab +1Blood Glucose Forecasting
Technical University of Munich, Munich, Germany · University Hospital Augsburg, Augsburg, Germany · Munich Center for Machine Learning (MCML), Munich, Germany +1
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
It's All in the Way You Say It: The Role of Information Representation in LLM-Based Glycemic-Event Prediction
Sep 8, 2026Andrea Apicella, Pasquale Arpaia, Matteo Orefice +2LLM Prompting Blood Glucose Forecasting
Department of Information Engineering, Electrical Engineering, and Applied Mathematics (DIEM), University of Salerno, Fisciano (SA), Italy · Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
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
GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes
Jul 23, 2026Davide Marelli, Giorgia Rigamonti, Mirko Paolo Barbato +1Type 1 Diabetes Time Series Forecasting
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano 20126, Italy
Subject-Conditioned Glucose Forecasting in Type-1 Diabetes
Jul 21, 2026Giorgia Rigamonti, Mirko Paolo Barbato, Davide Marelli +1Type 1 Diabetes Time Series Forecasting
Department of Informatics, Systems and Communication University of Milano-Bicocca Milan, Italy
Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization
Jun 28, 2026Amirreza Dolatpour Fathkouhi, Justin Lee, Heman ShakeriFlow Matching Type 1 Diabetes
Department of Computer Science · School of Data Science
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
LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
Jun 10, 2026Yifan Gao, Yanmin Gong, Yun Shi +1Type 1 Diabetes Time Series Forecasting
Department of Information Systems and Cybersecurity The University of Texas at San Antonio San Antonio, USA · School of Engineering Medicine Texas A&M University Houston, USA · Department of Family and Community Medicine The University of Texas at San Antonio San Antonio, USA
GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting
Jun 5, 2026Baiying Lu, Zhaohui Liang, Ryan Pontius +2Zero-Shot Time Series Forecasting Time Series Forecasting
Department of Computer Science Dartmouth College Hanover, NH 03784 · Department of Computer Science Emory University Atlanta, GA 30322 · Quantitative Biomedical Sciences Dartmouth College Hanover, NH 03784
Evidence-Guided Neural Architecture Selection under Uncertainty for Subject-Specific Blood Glucose Forecasting
Jun 3, 2026Md Azharul Islam, Dwyer Deighan, Tarunraj Singha +1Time Series Forecasting Blood Glucose Forecasting
Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA · Computational and Data-Enabled Sciences, University at Buffalo, Buffalo, NY, USA · University at Buffalo, Buffalo, NY, USA
PhysioSeq2Seq: A Hybrid Physiological Digital Twin and Sequence-to-Sequence LSTM for Long-Horizon Glucose Forecasting in Type 1 Diabetes
May 16, 2026Phat Tran, Neville Mehta, Clara Mosquera-Lopez +3Long-Term Time Series Forecasting Type 1 Diabetes
Oregon State University · Oregon Health & Science University
From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting
May 1, 2026Alireza Namazi, Heman ShakeriHealthcare Clinical Decision Support
Department of Computer Science University of Virginia Charlottesville, VA, USA · School of Data Science University of Virginia Charlottesville, VA, USA
Uncertainty quantification in neural network-based glucose prediction for diabetes
Mar 5, 2026Hai Siong Tan, Rafe McBethBlood Glucose Forecasting Uncertainty Calibration
Gryphon Center for A.I. and Theoretical Sciences, Singapore · University of Pennsylvania, Perelman School of Medicine, Philadelphia, 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

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Evaluating Type-1 Diabetes Digital Twins by Decision Quality

Victor Li, Owen Tucker, Michael S. Hughes, Temiloluwa Prioleau+1

Department of Computer Science, Emory University · School of Medicine, Emory University

Digital Twins Clinical Decision-Making Type 1 Diabetes Blood Glucose Forecasting

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A digital twin (DT) for type 1 diabetes (T1D) can predict how an individual's blood glucose will respond to different insulin treatments without actually trying them. For each treatment, downstream statistics of predicted glucose trajectories, such as glycemic risk, can be computed to guide clinical decisions. However, the quality of DTs is usually measured by the accuracy of their predicted glucose trajectories, which may not reflect the quality of the downstream treatment decisions they inform. In this work, we directly evaluate DTs by this decision quality on downstream treatment ranking, i.e., whether a DT predicts glucose trajectories that can be used to rank candidate treatments as the real patient would. We present an evaluation protocol over 30 virtual patients from the FDA-accepted UVA/Padova model, each simulated under 21 different basal-bolus treatments. For each patient, a DT is fitted under one treatment and then produces glucose trajectories for the remaining 20 treatments. Our results show that prediction accuracy and decision quality disagree. A baseline that models the average patient, never seeing the individual's glucose data, has the lowest prediction accuracy of all six methods (RMSE: 63.6 mg/dL) but ranks treatments with the second-highest mean Spearman (0.766), while a black-box neural network that predicts glucose nearly twice as accurately (RMSE: 32.9 mg/dL) ranks them no better than a random ordering of the 20 treatments (0.049 Spearman). Therefore, selecting a DT based on prediction accuracy alone can prefer one that recommends treatments at chance, and decision quality must be evaluated directly rather than assumed to follow from prediction accuracy.