GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks
Journal:
arXiv
Published Date:
Oct 8, 2024
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.
