Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification. (arXiv:2209.04526v1 [cs.LG])

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 …

arxiv forecasting personalized quantification transformer uncertainty