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

Digital Health Reviews (1487) Artificial Intelligence (1676) Machine Learning (1694) Diabetes Self-Management (527) Glucose Tracking and Self-Monitoring of Blood Glucose (179)

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
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Sven Awege Sven Awege @SVENNIECO

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 D… https://t.co/nojUdLGSP3

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JMIR Publications JMIR Publications @jmirpub

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Sid Ghodke Sid Ghodke @MultigrainSid

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SBMDigitalHealth SBMDigitalHealth @SBMDigitalHlth

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 D… https://t.co/BkFo13f3ff

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Saroj Humagain Saroj Humagain @msarozz

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HAS-veille HAS-veille @HAS_veille

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 D… https://t.co/KpKei29ZYS

3:34 PM · May 06, 2019

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Pagès Frédérique Pagès Frédérique @fredpages

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 D… https://t.co/iEzH9ZrE5F

3:34 PM · May 06, 2019

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PhilippeSayous PhilippeSayous @PhilippeSayous

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