论文标题
GLUFORMER：基于变压器的个性化葡萄糖预测和不确定性量化
Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification
论文作者
论文摘要
深度学习模型实现了最先进的结果，从而预测了血糖轨迹，并提出了广泛的体系结构。但是，这种模型在临床实践中的适应性很慢，这主要是由于缺乏对所提供的预测的不确定性量化。在这项工作中，我们建议将以过去的条件为基础分布的无限混合物（即高斯，拉普拉斯等）建模为未来的葡萄糖轨迹。这种变化使我们能够学习不确定性并在轨迹具有异质或多模式分布的情况下更准确地预测。为了估计预测分布的参数，我们利用了变压器体系结构。我们从经验上证明了我们方法在合成和基准葡萄糖数据集上的准确性和不确定性方面的优越性优于现有的最新技术。
Deep learning models achieve state-of-the art results in predicting blood glucose trajectories, with a wide range of architectures being proposed. However, the adaptation of such models in clinical practice is slow, largely due to the lack of uncertainty quantification of provided predictions. In this work, we propose to model the future glucose trajectory conditioned on the past as an infinite mixture of basis distributions (i.e., Gaussian, Laplace, etc.). This change allows us to learn the uncertainty and predict more accurately in the cases when the trajectory has a heterogeneous or multi-modal distribution. To estimate the parameters of the predictive distribution, we utilize the Transformer architecture. We empirically demonstrate the superiority of our method over existing state-of-the-art techniques both in terms of accuracy and uncertainty on the synthetic and benchmark glucose data sets.
