Uncertainty quantification in neural network-based glucose prediction for diabetes
cs.LG, physics.med-ph
Submitted: 2026-03-05
Updated: 2026-03-28
Comments: 20 pages, 7 figures; v2: minor revisions with PR-AUC curves included in result analysis. Code available at https://github.com/HaiSiong-Tan/Uncertainty_aware_glucose_prediction
Code: https://github.com/HaiSiong-Tan/Uncertainty_aware_glucose_prediction
License: http://creativecommons.org/licenses/by/4.0/
The gist: In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes.
Terminology
Abstract
In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes. We consider three families of sequence models based on LSTM, GRU, and Transformer architectures, with uncertainty quantification enabled by either Monte Carlo dropout or through evidential output layers compatible with Deep Evidential Regression. Using the HUPA-UCM diabetes dataset for validation, we find that Transformer-based models equipped with evidential output heads provide the most effective uncertainty-aware framework, achieving consistently higher predictive accuracies and better-calibrated uncertainty estimates whose magnitudes significantly correlate with prediction errors. We further evaluate the clinical risk of each model using the recently proposed Diabetes Technology Society error grid, with risk categories defined by international expert consensus. Our results demonstrate the value of integrating principled uncertainty quantification into real-time machine-learning-based blood glucose prediction systems.
Sources
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Evidential Deep Learning to Quantify Classification Uncertainty
- Evidential Physics-Informed Neural Networks
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