发表机构
Department of Informatics, Systems and Communication, University of Milano-Bicocca（米兰比可卡大学信息、系统与通信系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GlucoTune框架解决糖尿病血糖数据预处理等缺乏标准流程与评估协议问题，通过可配置管道实现可重复预处理，提供统一接口和基准测试排行榜，集成数据集，经实验验证有效。
AI 中文摘要
预处理血糖时间序列数据在开发糖尿病管理数据驱动方法中至关重要却常被忽视，尤其是1型糖尿病。缺乏标准化预处理流程和评估协议阻碍了可重复性及公平比较，数据共享限制更使问题恶化。为此，我们提出GlucoTune框架，它标准化了从预处理到模型评估的整个实验工作流程，通过可配置管道实现可重复预处理，提供统一接口用于实现、训练和评估血糖预测模型，集成公共数据集并提供基准测试排行榜，通过实验证明了其有效性。
英文摘要
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.
