GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes

2026-07-23

AI Summary

This work addresses the lack of standardized preprocessing and unified evaluation protocols in blood glucose time series research by proposing an end-to-end reproducible analysis framework. The framework introduces a novel YAML configuration–driven preprocessing mechanism that eliminates the need for distributing sensitive data, integrates standardized dataset wrappers, provides a unified model interface, and incorporates multiple glucose- and time series–specific prediction algorithms. Furthermore, it establishes the first public benchmark platform and leaderboard dedicated to glucose forecasting. Experimental results and user studies demonstrate that the framework substantially enhances model reproducibility, cross-study comparability, and evaluation transparency.

Abstract

Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes management, particularly for type 1 diabetes. The lack of standardized preprocessing workflows and evaluation protocols hinders reproducibility and complicates fair comparison across studies. These challenges are further exacerbated by data-sharing restrictions, as privacy and licensing constraints often prevent the redistribution of preprocessed medical datasets. To address these limitations, we present GlucoTune, a comprehensive and extensible framework for reproducible experimentation with blood glucose time-series data. The framework standardizes the entire experimental workflow, from preprocessing to model evaluation, enabling reproducible experiments directly from the original datasets. Reproducible preprocessing is achieved through configurable pipelines defined in portable YAML configuration files, ensuring consistent data handling without distributing sensitive preprocessed data. Beyond preprocessing, GlucoTune provides a unified interface for implementing, training, and evaluating blood glucose prediction models. The framework integrates public datasets through standardized wrappers and provides a curated collection of state-of-the-art blood glucose prediction and general time-series forecasting methods, while remaining readily extensible to additional datasets, preprocessing strategies, and forecasting models. To promote transparent and consistent evaluation, GlucoTune includes a benchmarking leaderboard that reports results across datasets, preprocessing configurations, and forecasting methods, enabling systematic comparison of experimental settings. We demonstrate the effectiveness of GlucoTune through comprehensive experiments and assess its usability in a user study.

Problem

• blood glucose preprocessing
• reproducibility
• standardized evaluation
• data sharing restrictions
• diabetes management

Innovation

• blood glucose forecasting
• time-series preprocessing
• reproducible research
• benchmarking framework
• diabetes management