# A deep learning approach to diabetic blood glucose prediction

arXiv: https://arxiv.org/abs/1707.05828 · HTML: https://mlsanity.com/paper/1707.05828/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2017.

## Built on by (most cited first, 8 of 8)

- [Tractability of approximation by general shallow networks](https://mlsanity.com/paper/2308.03230/) (2023)
- [A function approximation approach to the prediction of blood glucose levels](https://mlsanity.com/paper/2105.05893/) (2021)
- [Neural Spline Search for Quantile Probabilistic Modeling](https://mlsanity.com/paper/2301.04857/) (2023)
- [Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin](https://mlsanity.com/paper/2401.02661/) (2024)
- [Recursive input and state estimation: A general framework for learning from time series with missing data](https://mlsanity.com/paper/2104.08556/) (2021)
- [Local transfer learning from one data space to another](https://mlsanity.com/paper/2302.00160/) (2023)
- [GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes](https://mlsanity.com/paper/2607.21117/) (2026)
- [Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting](https://mlsanity.com/paper/2609.11872/) (2026)

## Abstract (arXiv)

We consider the question of 30-minute prediction of blood glucose levels measured by continuous glucose monitoring devices, using clinical data. While most studies of this nature deal with one patient at a time, we take a certain percentage of patients in the data set as training data, and test on the remainder of the patients; i.e., the machine need not re-calibrate on the new patients in the data set. We demonstrate how deep learning can outperform shallow networks in this example. One novelty is to demonstrate how a parsimonious deep representation can be constructed using domain knowledge.