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

arXiv: https://arxiv.org/abs/2506.07864 · HTML: https://mlsanity.com/paper/2506.07864/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition](https://mlsanity.com/paper/2109.07270/) (2021)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [Convolutional Recurrent Neural Networks for Glucose Prediction](https://mlsanity.com/paper/1807.03043/) (2018)
- [Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification](https://mlsanity.com/paper/2209.04526/) (2022)
- [DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions](https://mlsanity.com/paper/2304.06506/) (2023)

## Built on by (most cited first, 2 of 2)

- [Subject-Conditioned Glucose Forecasting in Type-1 Diabetes](https://mlsanity.com/paper/2607.19006/) (2026)
- [GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes](https://mlsanity.com/paper/2607.21117/) (2026)

## Abstract (arXiv)

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.