# Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2106.00884 · HTML: https://mlsanity.com/paper/2106.00884/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories](https://mlsanity.com/paper/1806.05357/) (2018)
- [Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network](https://mlsanity.com/paper/1809.03817/) (2018)
- [Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity](https://mlsanity.com/paper/2010.07349/) (2020)

## Built on by (most cited first, 7 of 7)

- [Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification](https://mlsanity.com/paper/2209.04526/) (2022)
- [GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks](https://mlsanity.com/paper/2410.05780/) (2024)
- [Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting](https://mlsanity.com/paper/2411.10703/) (2024)
- [Features Fusion Framework for Multimodal Irregular Time-series Events](https://mlsanity.com/paper/2209.01728/) (2022)
- [The Driver-Blindness Phenomenon: Why Deep Sequence Models Default to Autocorrelation in Blood Glucose Forecasting](https://mlsanity.com/paper/2511.20601/) (2025)
- [A Review on Machine Learning Approaches for the Prediction of Glucose Levels and Hypogylcemia](https://mlsanity.com/paper/2601.11615/) (2026)
- [FairGlucose: A CGM Fairness Benchmark Reveals Subgroup Disparities Hidden in Population-Level Validation](https://mlsanity.com/paper/2608.18296/) (2026)

## Abstract (arXiv)

In this paper, we study the problem of blood glucose forecasting and provide a deep personalized solution. Predicting blood glucose level in people with diabetes has significant value because health complications of abnormal glucose level are serious, sometimes even leading to death. Therefore, having a model that can accurately and quickly warn patients of potential problems is essential. To develop a better deep model for blood glucose forecasting, we analyze the data and detect important patterns. These observations helped us to propose a method that has several key advantages over existing methods: 1- it learns a personalized model for each patient as well as a global model; 2- it uses an attention mechanism and extracted time features to better learn long-term dependencies in the data; 3- it introduces a new, robust training procedure for time series data. We empirically show the efficacy of our model on a real dataset.