Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data

Autor: Morten Lind Jensen, Alexander Rosenberg Johansen, Nicklas Hansen, Jens M. Tarp, Peter Ebert Christensen, Morten Mørup, Ali Mohebbi, Henrik Bengtsson

Publikováno v: EMBC
Mohebbi, A, Johansen, A R, Hansen, N, Christensen, P E, Tarp, J M, Jensen, M L, Bengtsson, H & Morup, M 2020, Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data . in Proceedings of 42 nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society : Enabling Innovative Technologies for Global Healthcare, EMBC 2020 ., 9176695, IEEE, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2020-July, pp. 5140-5145, 42 nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Montreal, Quebec, Canada, 20/07/2020 . https://doi.org/10.1109/EMBC44109.2020.9176695

Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9254262a5909840693034f7139cb957f
https://doi.org/10.1109/embc44109.2020.9176695