Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes

TL;DR

This paper introduces a lightweight Transformer-based model for blood glucose prediction in Type-1 Diabetes, optimized for wearable devices, and demonstrates superior performance on benchmark datasets.

Contribution

It presents a novel, resource-efficient Transformer architecture tailored for real-time blood glucose prediction on edge devices, combining attention mechanisms with sequential modeling.

Findings

Outperforms existing methods in glucose level prediction

Effective in detecting hypo- and hyperglycemic events

Optimized for deployment on resource-constrained devices

Abstract

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.