# Convolutional Recurrent Neural Networks for Glucose Prediction

arXiv: https://arxiv.org/abs/1807.03043 · HTML: https://mlsanity.com/paper/1807.03043/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 20 of 20)

- [DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions](https://mlsanity.com/paper/2304.06506/) (2023)
- [R2GenCSR: Mining Contextual and Residual Information for LLMs-based Radiology Report Generation](https://mlsanity.com/paper/2408.09743/) (2024)
- [GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks](https://mlsanity.com/paper/2410.05780/) (2024)
- [Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction](https://mlsanity.com/paper/2101.06850/) (2021)
- [Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting](https://mlsanity.com/paper/2411.10703/) (2024)
- [A Large Sensor Foundation Model Pretrained on Continuous Glucose Monitor Data for Diabetes Management](https://mlsanity.com/paper/2412.09727/) (2024)
- [Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes](https://mlsanity.com/paper/2502.14183/) (2025)
- [EMRRG: Efficient Fine-Tuning Pre-trained X-ray Mamba Networks for Radiology Report Generation](https://mlsanity.com/paper/2510.16776/) (2025)
- [Global Deep Forecasting with Patient-Specific Pharmacokinetics](https://mlsanity.com/paper/2309.13135/) (2023)
- [CrossGP: Cross-Day Glucose Prediction Excluding Physiological Information](https://mlsanity.com/paper/2404.10901/) (2024)
- [Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes](https://mlsanity.com/paper/2506.07864/) (2025)
- [Long Short-term Cognitive Networks](https://mlsanity.com/paper/2106.16233/) (2021)
- [FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions](https://mlsanity.com/paper/2408.13926/) (2024)
- [Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutions](https://mlsanity.com/paper/2507.14077/) (2025)
- [RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting](https://mlsanity.com/paper/2106.04148/) (2021)
- [Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction](https://mlsanity.com/paper/2307.01238/) (2023)
- [Bridging Vision and Language: Optimal Transport-Driven Radiology Report Generation via LLMs](https://mlsanity.com/paper/2507.03908/) (2025)
- [GluMind: Multimodal Parallel Attention and Knowledge Retention for Robust Cross-Population Blood Glucose Forecasting](https://mlsanity.com/paper/2509.18457/) (2025)
- [PhysioSeq2Seq: A Hybrid Physiological Digital Twin and Sequence-to-Sequence LSTM for Long-Horizon Glucose Forecasting in Type 1 Diabetes](https://mlsanity.com/paper/2605.16860/) (2026)
- [GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes](https://mlsanity.com/paper/2607.21117/) (2026)

## Abstract (arXiv)

Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this work, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (RMSE = 9.38±0.71 [mg/dL] over a 30-minute horizon, RMSE = 18.87±2.25 [mg/dL] over a 60-minute horizon) and real patient cases (RMSE = 21.07±2.35 [mg/dL] for 30-minute, RMSE = 33.27±4.79% for 60-minute). In addition, the model provides competitive performance in providing effective prediction horizon (PHeff) with minimal time lag both in a simulated patient dataset (PHeff = 29.0±0.7 for 30-min and PHeff = 49.8±2.9 for 60-min) and in a real patient dataset (PHeff = 19.3±3.1 for 30-min and PHeff = 29.3±9.4 for 60-min). This approach is evaluated on a dataset of 10 simulated cases generated from the UVa/Padova simulator and a clinical dataset of 10 real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four algorithms. The proposed algorithm is implemented on an Android mobile phone, with an execution time of 6ms on a phone compared to an execution time of 780ms on a laptop.