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

Machine Learning (1434) Artificial Intelligence in Diabetes Care and Prevention (27) Decision Support for Health Professionals (1163) Clinical Information and Decision Making (1374) Secondary Use of Clinical Data for Research and Surveillance (358) Artificial Intelligence (1328)

Published on in Vol 9, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25237, first published .
Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records

Authors of this article:

Zakhriya Alhassan1, 2 Author Orcid Image ;   Matthew Watson1 Author Orcid Image ;   David Budgen1 Author Orcid Image ;   Riyad Alshammari3 Author Orcid Image ;   Ali Alessa4 Author Orcid Image ;   Noura Al Moubayed1 Author Orcid Image
Article Authors Cited by (6) Tweetations (5) Metrics

Zakhriya Alhassan   1, 2 , PhD ;   Matthew Watson   1 , MSc ;   David Budgen   1 , PhD ;   Riyad Alshammari   3 , PhD ;   Ali Alessa   4 , PhD ;   Noura Al Moubayed   1 , PhD

1 Department of Computer Science, Durham University, Durham, United Kingdom

2 College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia

3 National Center for Artificial Intelligence, Saudi Data and Artificial Intelligence Authority, Riyadh, Saudi Arabia

4 Department of Information Technology Programs, Institute of Public Administration, Riyadh, Saudi Arabia

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