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

M Tallon, Erin; D Williams, David; Schweisberger, Cintya; Mullaney, Colin; Lockee, Brent; Ferro, Diana; A Vandervelden, Craig; S Barnes, Mitchell; Cristello Sarteau, Angelica; R Kahkoska, Anna; R Patton, Susana; Mehta, Sanjeev; McDonough, Ryan; Lind, Marcus; D'Avolio, Leonard; A Clements, Mark

doi:10.2196/69142

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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

Shared by NobleBlocks on Sep 25, 2025 • 12:00 AM UTC

Authors:

Erin M Tallon

David D Williams

Cintya Schweisberger

Abstract

Abstract Background Clinicians currently lack an effective means for identifying youth with type 1 diabetes (T1D) who are at risk for experiencing glycemic deterioration between diabetes clinic visits. As a result, their ability to identify youth who may optimally benefit from targeted interventions...

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