Blood Glucose Prediction with Variance Estimation Using Recurrent Neural Networks
Authors: John Martinsson, Alexander Schliep, Björn Eliasson, Olof Mogren
Published in: Journal of Healthcare Informatics Research
Year: 2020
Location:
Abstract
In this work, we present an approach for predicting blood glucose levels for diabetics up to one hour into the future. The approach is based on recurrent neural networks trained in an end-to-end fashion, requiring nothing but the glucose level history for the patient. The model outputs the prediction along with an estimate of its certainty, helping users to interpret the predicted levels. The approach needs no feature engineering or data pre-processing, and is computationally inexpensive.
BibTeX
@article{Martinsson2020,
author = {Martinsson, John and Schliep, Alexander and Eliasson, Bj{\"{o}}rn and Mogren, Olof},
title = {Blood Glucose Prediction with Variance Estimation Using Recurrent Neural Networks},
journal = {Journal of Healthcare Informatics Research},
year = {2020},
volume = {4},
number = {1},
pages = {1--18},
doi = {10.1007/s41666-019-00059-y},
url = {https://link.springer.com/article/10.1007/s41666-019-00059-y}
}