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

Renat Sergazinov, Elizabeth Chun, Valeriya Rogovchenko, Nathaniel J Fernandes, Nicholas Kasman, Irina Gaynanova

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Keywords: diabetes management, continuous glucose monitors (CGM), glucose trajectory prediction, artificial pancreas systems, public datasets, standardized tasks, benchmark models, glycemic control

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TL;DR: The paper introduces a comprehensive resource for CGM-based glucose forecasting, including a curated list of public datasets, a standardized task list, and benchmark models.

Abstract:

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.

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Primary Area: datasets and benchmarks

Submission Number: 5569