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

Artificial Intelligence in Diabetes Care and Prevention (46) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1683) Diabetes Self-Management (571) Machine Learning (1984) Electronic Health Records (1278) Diabetes Education and Self-Management in Childhood and Adolescence (43) Decision Support for Health Professionals (1479)

Published on in Vol 10 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69142, first published .
Toward a Clinically Actionable, Electronic Health Record–Based Machine Learning Model to Forecast 90-Day Change in Hemoglobin A1c in Youth With Type 1 Diabetes: Feasibility and Model Development Study

Toward a Clinically Actionable, Electronic Health Record–Based Machine Learning Model to Forecast 90-Day Change in Hemoglobin A1c in Youth With Type 1 Diabetes: Feasibility and Model Development Study

Toward a Clinically Actionable, Electronic Health Record–Based Machine Learning Model to Forecast 90-Day Change in Hemoglobin A1c in Youth With Type 1 Diabetes: Feasibility and Model Development Study

Authors of this article:

Erin M Tallon1, 2, 3 Author Orcid Image ;   David D Williams4 Author Orcid Image ;   Cintya Schweisberger1, 2 Author Orcid Image ;   Colin Mullaney5 Author Orcid Image ;   Brent Lockee1 Author Orcid Image ;   Diana Ferro6 Author Orcid Image ;   Craig A Vandervelden1 Author Orcid Image ;   Mitchell S Barnes1 Author Orcid Image ;   Angelica Cristello Sarteau7 Author Orcid Image ;   Anna R Kahkoska7 Author Orcid Image ;   Susana R Patton8 Author Orcid Image ;   Sanjeev Mehta9 Author Orcid Image ;   Ryan McDonough1, 2, 10 Author Orcid Image ;   Marcus Lind11, 12, 13 Author Orcid Image ;   Leonard D'Avolio5, 14, 15 Author Orcid Image ;   Mark A Clements1, 2 Author Orcid Image
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