轻量级Transformer模型提升糖尿病血糖预测准确率
Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes
- 融合注意力与序列建模，兼顾长期依赖与计算效率
- 在两个数据集上优于现有方法，事件检测更精准
- 专为可穿戴设备设计，适合临床实时监测场景
1型糖尿病（T1D）影响全球数百万人，需持续监测以防止严重低血糖和高血糖事件。尽管连续葡萄糖监测已改善血糖管理，但在可穿戴设备上部署预测模型仍面临计算和内存限制。为此，我们提出一种新型轻量级序列Transformer模型，用于T1D血糖预测。该模型结合Transformer的注意力机制与循环神经网络的序列处理能力，有效捕捉长期依赖关系，同时保持计算高效性。模型针对资源受限的边缘设备优化，并采用平衡损失函数应对低/高血糖事件的数据不平衡问题。在两个基准数据集OhioT1DM和DiaTrend上的实验表明，该模型在血糖预测及不良事件检测方面均优于现有先进方法。本工作填补了高性能建模与实际部署之间的空白，提供了一种可靠且高效的T1D管理解决方案。
原文摘要 · Abstract (English)
Type 1 Diabetes (T1D) affects millions worldwide, requiring continuous monitoring to prevent severe hypo- and hyperglycemic events. While continuous glucose monitoring has improved blood glucose management, deploying predictive models on wearable devices remains challenging due to computational and memory constraints. To address this, we propose a novel Lightweight Sequential Transformer model designed for blood glucose prediction in T1D. By integrating the strengths of Transformers' attention mechanisms and the sequential processing of recurrent neural networks, our architecture captures long-term dependencies while maintaining computational efficiency. The model is optimized for deployment on resource-constrained edge devices and incorporates a balanced loss function to handle the inherent data imbalance in hypo- and hyperglycemic events. Experiments on two benchmark datasets, OhioT1DM and DiaTrend, demonstrate that the proposed model outperforms state-of-the-art methods in predicting glucose levels and detecting adverse events. This work fills the gap between high-performance modeling and practical deployment, providing a reliable and efficient T1D management solution.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。