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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.

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Title: 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: Tallon EM; Division of Pediatric Endocrinology and Diabetes, Children's Mercy Kansas City, 2401 Gillham Road, Kansas City, MO, United States, 1 8166014023.; Department of Pediatrics, UMKC School of Medicine, Kansas City, MO, United States.; Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO, United States., Williams DD; Division of Health Services and Outcomes Research, Children's Mercy Kansas City, Kansas City, MO, United States., Schweisberger C; Division of Pediatric Endocrinology and Diabetes, Children's Mercy Kansas City, 2401 Gillham Road, Kansas City, MO, United States, 1 8166014023.; Department of Pediatrics, UMKC School of Medicine, Kansas City, MO, United States., Mullaney C; Blue Circle Health, Boston, MA, United States., Lockee B; Division of Pediatric Endocrinology and Diabetes, Children's Mercy Kansas City, 2401 Gillham Road, Kansas City, MO, United States, 1 8166014023., Ferro D; Preventive and Predictive Medicine, IRCCS, Bambino Gesù Children's Hospital, Rome, Italy., Vandervelden CA; Division of Pediatric Endocrinology and Diabetes, Children's Mercy Kansas City, 2401 Gillham Road, Kansas City, MO, United States, 1 8166014023., Barnes MS; Division of Pediatric Endocrinology and Diabetes, Children's Mercy Kansas City, 2401 Gillham Road, Kansas City, MO, United States, 1 8166014023., Sarteau AC; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States., Kahkoska AR; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States., Patton SR; Center for Healthcare Delivery Science, Nemours Children's Health, Jacksonville, FL, United States., Mehta S; Joslin Diabetes Center, Boston, MA, United States., McDonough R; Division of Pediatric Endocrinology and Diabetes, Children's Mercy Kansas City, 2401 Gillham Road, Kansas City, MO, United States, 1 8166014023.; Department of Pediatrics, UMKC School of Medicine, Kansas City, MO, United States.; Department of Pediatrics, Arkansas Children's Northwest, Springdale, AR, United States., Lind M; Department of Medicine, NU-Hospital Group, Uddevalla, Sweden.; Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden.; Department of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden., D'Avolio L; Blue Circle Health, Boston, MA, United States.; Harvard Medical School, Boston, MA, United States.; Mass General Brigham, Boston, MA, United States., Clements MA; Division of Pediatric Endocrinology and Diabetes, Children's Mercy Kansas City, 2401 Gillham Road, Kansas City, MO, United States, 1 8166014023.; Department of Pediatrics, UMKC School of Medicine, Kansas City, MO, United States.
Source: JMIR diabetes [JMIR Diabetes] 2025 Sep 25; Vol. 10, pp. e69142. Date of Electronic Publication: 2025 Sep 25.
Publication Type: Journal Article
Journal Info: Publisher: JMIR Publications Inc Country of Publication: Canada NLM ID: 101719410 Publication Model: Electronic Cited Medium: Internet ISSN: 2371-4379 (Electronic) Linking ISSN: 23714379 NLM ISO Abbreviation: JMIR Diabetes Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: Toward a Clinically Actionable, Electronic Health Record-Based Machine Learning Model to Forecast 90-Day Change in Hemoglobin A<subscript>1c</subscript> in Youth With Type 1 Diabetes: Feasibility and Model Development Study.
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