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

Artificial Intelligence in Diabetes Care and Prevention (66) Clinical Informatics (1483) Clinical Information and Decision Making (2138) Decision Support for Health Professionals (1686) Diabetes (292) Diabetes-specific EHR Improvements (35) Secondary Use of Clinical Data for Research and Surveillance (503) Diabetes Health Services and System Innovations (149)

Published on in Vol 9 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53338, first published .
A Machine Learning Model for Risk Stratification of Postdiagnosis Diabetic Ketoacidosis Hospitalization in Pediatric Type 1 Diabetes: Retrospective Study

A Machine Learning Model for Risk Stratification of Postdiagnosis Diabetic Ketoacidosis Hospitalization in Pediatric Type 1 Diabetes: Retrospective Study

A Machine Learning Model for Risk Stratification of Postdiagnosis Diabetic Ketoacidosis Hospitalization in Pediatric Type 1 Diabetes: Retrospective Study

Authors of this article:

Devika Subramanian1 Author Orcid Image ;   Rona Sonabend2, 3 Author Orcid Image ;   Ila Singh4, 5 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Tallon E, Williams D, Schweisberger C, Mullaney C, Lockee B, Ferro D, Vandervelden C, Barnes M, Sarteau A, Kahkoska A, Patton S, Mehta S, McDonough R, Lind M, D'Avolio L, Clements M. 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. JMIR Diabetes 2025;10:e69142 View
  2. Kohlenberg J, Xu M, Coopergard R, Helgeson E, Gross A, Mathioudakis N, Vandervelden C, Clements M, Chow L, Ma S. The Development and Validation of Multivariable Electronic Health Record-Based Models to Predict Diabetic Ketoacidosis-Related Hospitalizations for Adults with Type 1 Diabetes. Diabetes Technology & Therapeutics 2026;28(5):415 View
  3. Fllatah J, Banjar H. Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines. JMIR Formative Research 2026;10:e81039 View