评估时序基础模型和多模态饮食上下文对CGM预测的影响
Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
这篇论文研究的是如何用AI模型更准确地预测血糖，对糖尿病患者管理很有用。他们测试了Chronos-Bolt模型，发现微调后效果比直接用好很多，还结合了饮食数据，能更精准地预测餐后血糖变化。
研究使用八组公开的CGM数据集，比较了时序基础模型（如Chronos-Bolt）和传统方法（如Elastic Net、PatchTST）在血糖预测中的表现。结果显示，在零样本情况下，基础模型并未始终优于任务专用基线，但轻量级微调能显著提升性能，例如Chronos-Bolt在T1D群体中可将RMSE降低6.5%-18.4%。同时，结合饮食信息的多模态融合框架（如CGMacros）也带来了改进，整体RMSE降低约3%，餐后RMSE降低约15%，表明饮食信息提供了CGM之外的、具有临床意义的信号。
Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
Continuous glucose monitoring (CGM) provides high-frequency measurements of glucose dynamics and enables short-term glucose forecasting for diabetes management. Although time-series foundation models have shown strong general forecasting ability, their effectiveness for CGM prediction and the added value of multimodal dietary context remain unclear. We conduct a comprehensive empirical study using eight public CGM datasets spanning Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol across multiple context lengths and prediction horizons, zero-shot foundation models did not consistently outperform strong task-specific baselines such as Elastic Net and PatchTST. In contrast, lightweight fine-tuning substantially improved forecasting performance. For example, fine-tuned Chronos-Bolt reduced RMSE by 6.5%-18.4% in the T1D cohort and by 8.6%-18.2% in the non-diabetes/T2D cohort, with comparable improvements in both in-distribution and out-of-distribution test settings. We further evaluate multimodal dietary context using CGMacros, which provides temporally aligned CGM signals, food images, and macronutrient records. A residual-based fusion framework reduced overall RMSE by approximately 3% and postprandial RMSE by approximately 15% relative to the CGM-only baseline. Moreover, Chronos-based CGM representations were more strongly correlated with observed postprandial glucose increments than representations from LSTM and CatBoost, even after those models incorporated additional dietary modalities, suggesting that pretrained temporal representations better preserve meal-induced excursion patterns. These findings show that foundation models require CGM-specific adaptation for reliable forecasting and that dietary context provides clinically meaningful signals beyond CGM alone, especially during postprandial periods.
