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

Ashenafi Zebene Woldaregay   1 , MSc ;   Eirik Årsand   2 , PhD ;   Taxiarchis Botsis   3 , PhD ;   David Albers   4 , PhD ;   Lena Mamykina   4 , PhD ;   Gunnar Hartvigsen   1 , PhD

1 Department of Computer Science, University of Tromsø – The Arctic University of Norway, Tromsø, Norway

2 Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway

3 The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States

4 Department of Biomedical Informatics, Columbia University, New York, NY, United States

Corresponding Author:

  • Ashenafi Zebene Woldaregay, MSc
  • Department of Computer Science
  • University of Tromsø – The Arctic University of Norway
  • Realfagbygget, Hansine Hansens vei 54
  • Tromsø
  • Norway
  • Phone: 47 77646444
  • Email: ashenafi.z.woldaregay@uit.no