AI 中文总结
研究针对1型糖尿病血糖预测问题，提出多模态深度学习架构SCGP，基于观测数据和个体表征预测血糖，通过分离特征与建模避免早期融合，实验证明其能提高预测性能，利于个性化糖尿病管理。
AI 中文摘要
准确预测血糖浓度是1型糖尿病管理的关键，有助于早期发现不良血糖事件并支持及时治疗干预。尽管近期血糖预测有进展，但多数现有方法依赖群体层面表征或隐性个性化策略，无法进行有效的个体特异性预测。本文提出基于个体条件的血糖预测（SCGP），这是一种用于个性化血糖预测的新型多模态深度学习架构。SCGP基于观测到的血糖数据和从上下文信息中学习到的紧凑个体特异性表征来进行血糖预测。通过明确分离个体特征与血糖动态建模并避免异构输入的早期融合，该框架有效捕捉个体间差异，同时保持稳健可靠的时间建模。在两个最先进的基准数据集上的实验表明，SCGP持续提高预测性能，能够在多个预测范围内可靠检测不良血糖事件，突出了明确个体条件对个性化糖尿病管理的益处。
英文摘要
Accurate forecasting of blood glucose concentration is key in the management of Type 1 Diabetes, facilitating early detection of adverse glycemic events and supporting timely therapeutic interventions. Despite recent advances in glucose prediction, most existing approaches rely on population-level representations or implicit personalization strategies that fail to deliver effective subject-specific forecasts. In this work, we propose Subject-Conditioned Glucose Prediction (SCGP), a novel multimodal deep learning architecture conceived for personalized blood glucose prediction. SCGP conditions glucose predictions based on observed glucose data and a compact subject-specific representation learned from contextual information. By explicitly separating subject characterization from glucose dynamics modeling and avoiding early fusion of heterogeneous inputs, the proposed framework effectively captures inter-subject variability while preserving robust and reliable temporal modeling. Experiments on two state-of-the-art benchmark datasets demonstrate that SCGP consistently improves forecasting performance, enabling reliable detection of adverse glycemic events across multiple prediction horizons, highlighting the benefits of explicit subject conditioning for personalized diabetes management.
CommentsAccepted at the IEEE EMBC 2026 Conference
