This paper is in the following e-collection/theme issue:

Digital Health Reviews (1315) Artificial Intelligence (1407) Machine Learning (1488) Diabetes Self-Management (491) Glucose Tracking and Self-Monitoring of Blood Glucose (165)

Published on in Vol 21, No 5 (2019): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11030, first published .
Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Authors of this article:

Ashenafi Zebene Woldaregay1 Author Orcid Image ;   Eirik Årsand2 Author Orcid Image ;   Taxiarchis Botsis3 Author Orcid Image ;   David Albers4 Author Orcid Image ;   Lena Mamykina4 Author Orcid Image ;   Gunnar Hartvigsen1 Author Orcid Image
Article Authors Cited by (106) Tweetations (9) Metrics
All Time | Last Month | Last Six Months | Last Year
There are currently no tweets available for this article within the specified range.