用可信度量化提升糖尿病血糖预测准确率与风险预警能力
Uncertainty quantification in neural network-based glucose prediction for diabetes
- 采用证据化输出层融合Transformer模型实现不确定性建模
- 预测误差与不确定度显著相关，校准效果优于传统方法
- 适合临床决策支持系统，助力实时血糖风险预警
本文研究了用于1型糖尿病患者血糖预测及低血糖事件识别的不确定性感知神经网络模型。基于LSTM、GRU和Transformer三种序列架构，结合蒙特卡洛丢弃或与深度证据回归兼容的证据输出层实现不确定性量化。在HUPA-UCM糖尿病数据集上验证，采用证据输出头的Transformer模型表现最优，不仅预测精度更高，且不确定性估计更准确，其大小与预测误差显著相关。进一步使用糖尿病技术学会误差网格评估临床风险，依据国际专家共识划分风险等级。结果表明，将严谨的不确定性量化融入实时机器学习血糖预测系统具有重要价值。
原文摘要 · Abstract (English)
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.
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