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
Article Authors Cited by Tweetations Metrics

Erin M Tallon   1, 2, 3 * , PhD, RN ;   David D Williams   4 * , MPH ;   Cintya Schweisberger   1, 2 , DO ;   Colin Mullaney   5 , BS ;   Brent Lockee   1 , MS ;   Diana Ferro   6 , MSc, PhD ;   Craig A Vandervelden   1 , PhD ;   Mitchell S Barnes   1 , BS ;   Angelica Cristello Sarteau   7 , MSPH, PhD ;   Anna R Kahkoska   7 , MD, PhD ;   Susana R Patton   8 , PhD, CDCES ;   Sanjeev Mehta   9 , MD, MPH ;   Ryan McDonough   1, 2, 10 , DO ;   Marcus Lind   11, 12, 13 , MD, PhD ;   Leonard D'Avolio   5, 14, 15 , PhD ;   Mark A Clements   1, 2 , MD, PhD

1 Division of Pediatric Endocrinology and Diabetes, Children's Mercy Kansas City, Kansas City, MO, United States

2 Department of Pediatrics, UMKC School of Medicine, Kansas City, MO, United States

3 Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO, United States

4 Division of Health Services and Outcomes Research, Children's Mercy Kansas City, Kansas City, MO, United States

5 Blue Circle Health, Boston, MA, United States

6 Preventive and Predictive Medicine, IRCCS, Bambino Gesù Children's Hospital, Rome, Italy

7 Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

8 Center for Healthcare Delivery Science, Nemours Children's Health, Jacksonville, FL, United States

9 Joslin Diabetes Center, Boston, MA, United States

10 Department of Pediatrics, Arkansas Children's Northwest, Springdale, AR, United States

11 Department of Medicine, NU-Hospital Group, Uddevalla, Sweden

12 Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden

13 Department of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden

14 Harvard Medical School, Boston, MA, United States

15 Mass General Brigham, Boston, MA, United States

*these authors contributed equally

Corresponding Author:

  • Erin M Tallon, PhD, RN
  • Division of Pediatric Endocrinology and Diabetes
  • Children's Mercy Kansas City
  • 2401 Gillham Road
  • Kansas City, MO
  • United States
  • Phone: 1 8166014023
  • Email: etallon@cmh.edu