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

Digital Health Reviews (1289) Artificial Intelligence (1371) Machine Learning (1465) Diabetes Self-Management (484) Glucose Tracking and Self-Monitoring of Blood Glucose (162)

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 (105) Tweetations (9) Metrics

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