# GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks

arXiv: https://arxiv.org/abs/2410.05780 · HTML: https://mlsanity.com/paper/2410.05780/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 86 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Accurate Uncertainties for Deep Learning Using Calibrated Regression](https://mlsanity.com/paper/1807.00263/) (2018)
- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://mlsanity.com/paper/2201.12886/) (2022)
- [Darts: User-Friendly Modern Machine Learning for Time Series](https://mlsanity.com/paper/2110.03224/) (2021)
- [Convolutional Recurrent Neural Networks for Glucose Prediction](https://mlsanity.com/paper/1807.03043/) (2018)
- [Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)](https://mlsanity.com/paper/2209.07263/) (2022)
- [Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification](https://mlsanity.com/paper/2209.04526/) (2022)
- [Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories](https://mlsanity.com/paper/1806.05357/) (2018)
- [Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network](https://mlsanity.com/paper/1809.03817/) (2018)
- [Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks](https://mlsanity.com/paper/2106.00884/) (2021)
- [Enhancing Blood Glucose Prediction with Meal Absorption and Physical Exercise Information](https://mlsanity.com/paper/1901.07467/) (2018)

## Built on by (most cited first, 11 of 11)

- [Presenting DiaData for Research on Type 1 Diabetes](https://mlsanity.com/paper/2508.09160/) (2025)
- [Automatic selection of the best neural architecture for time series forecasting](https://mlsanity.com/paper/2501.12215/) (2025)
- [Benchmarking ResNet for Short-Term Hypoglycemia Classification with DiaData](https://mlsanity.com/paper/2511.02849/) (2025)
- [SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management](https://mlsanity.com/paper/2510.04386/) (2025)
- [From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting](https://mlsanity.com/paper/2605.00645/) (2026)
- [Personal Care Utility: Health as Everyday Infrastructure](https://mlsanity.com/paper/2606.14145/) (2026)
- [MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes](https://mlsanity.com/paper/2606.18640/) (2026)
- [FairGlucose: A CGM Fairness Benchmark Reveals Subgroup Disparities Hidden in Population-Level Validation](https://mlsanity.com/paper/2608.18296/) (2026)
- [Curriculum-Aware Interpolate-then-Refine: Learned Physiological Time-Series Imputation under Realistic Missingness](https://mlsanity.com/paper/2608.21207/) (2026)
- [HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care](https://mlsanity.com/paper/2609.06976/) (2026)
- [Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting](https://mlsanity.com/paper/2609.11872/) (2026)

## Abstract (arXiv)

The rising rates of diabetes necessitate innovative methods for its management. Continuous glucose monitors (CGM) are small medical devices that measure blood glucose levels at regular intervals providing insights into daily patterns of glucose variation. Forecasting of glucose trajectories based on CGM data holds the potential to substantially improve diabetes management, by both refining artificial pancreas systems and enabling individuals to make adjustments based on predictions to maintain optimal glycemic range.Despite numerous methods proposed for CGM-based glucose trajectory prediction, these methods are typically evaluated on small, private datasets, impeding reproducibility, further research, and practical adoption. The absence of standardized prediction tasks and systematic comparisons between methods has led to uncoordinated research efforts, obstructing the identification of optimal tools for tackling specific challenges. As a result, only a limited number of prediction methods have been implemented in clinical practice. To address these challenges, we present a comprehensive resource that provides (1) a consolidated repository of curated publicly available CGM datasets to foster reproducibility and accessibility; (2) a standardized task list to unify research objectives and facilitate coordinated efforts; (3) a set of benchmark models with established baseline performance, enabling the research community to objectively gauge new methods' efficacy; and (4) a detailed analysis of performance-influencing factors for model development. We anticipate these resources to propel collaborative research endeavors in the critical domain of CGM-based glucose predictions. {Our code is available online at github.com/IrinaStatsLab/GlucoBench.