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

Artificial Intelligence in Diabetes Care and Prevention (32) Clinical Information and Decision Making (1513) Machine Learning (1585) Decision Support for Health Professionals (1269) Artificial Intelligence (1556) Artificial Intelligence (111)

Published on in Vol 8 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47592, first published .
An “All-Data-on-Hand” Deep Learning Model to Predict Hospitalization for Diabetic Ketoacidosis in Youth With Type 1 Diabetes: Development and Validation Study

An “All-Data-on-Hand” Deep Learning Model to Predict Hospitalization for Diabetic Ketoacidosis in Youth With Type 1 Diabetes: Development and Validation Study

An “All-Data-on-Hand” Deep Learning Model to Predict Hospitalization for Diabetic Ketoacidosis in Youth With Type 1 Diabetes: Development and Validation Study

Authors of this article:

David D Williams1 Author Orcid Image ;   Diana Ferro2, 3 Author Orcid Image ;   Colin Mullaney4 Author Orcid Image ;   Lydia Skrabonja4 Author Orcid Image ;   Mitchell S Barnes2 Author Orcid Image ;   Susana R Patton5 Author Orcid Image ;   Brent Lockee2 Author Orcid Image ;   Erin M Tallon2 Author Orcid Image ;   Craig A Vandervelden2 Author Orcid Image ;   Cintya Schweisberger2 Author Orcid Image ;   Sanjeev Mehta6 Author Orcid Image ;   Ryan McDonough2 Author Orcid Image ;   Marcus Lind7, 8, 9 Author Orcid Image ;   Leonard D'Avolio4 Author Orcid Image ;   Mark A Clements2 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

Journals

  1. Ebekozien O, Mungmode A, Sanchez J, Rompicherla S, Demeterco-Berggren C, Weinstock R, Jacobsen L, Davis G, McKee A, Akturk H, Maahs D, Kamboj M. Longitudinal Trends in Glycemic Outcomes and Technology Use for Over 48,000 People with Type 1 Diabetes (2016–2022) from the T1D Exchange Quality Improvement Collaborative. Diabetes Technology & Therapeutics 2023;25(11):765 View
  2. Obaido G, Ogbuokiri B, Chukwu C, Osaye F, Egbelowo O, Uzochukwu M, Mienye I, Aruleba K, Primus M, Achilonu O. An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis. IEEE Access 2024;12:9536 View
  3. Teixeira P, Battelino T, Carlsson A, Gudbjörnsdottir S, Hannelius U, von Herrath M, Knip M, Korsgren O, Elding Larsson H, Lindqvist A, Ludvigsson J, Lundgren M, Nowak C, Pettersson P, Pociot F, Sundberg F, Åkesson K, Lernmark Å, Forsander G. Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data. Diabetologia 2024;67(6):985 View
  4. Nimri R, Phillip M, Clements M, Kovatchev B. Closed-Loop Control, Artificial Intelligence–Based Decision-Support Systems, and Data Science. Diabetes Technology & Therapeutics 2024;26(S1):S-68 View
  5. El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View
  6. Subramanian D, Sonabend R, Singh I. A Machine Learning Model for Risk Stratification of Postdiagnosis Diabetic Ketoacidosis Hospitalization in Pediatric Type 1 Diabetes: Retrospective Study. JMIR Diabetes 2024;9:e53338 View
  7. Vandervelden C, Lockee B, Barnes M, Tallon E, Williams D, Kahkoska A, Cristello Sarteau A, Patton S, Sonabend R, Kohlenberg J, Clements M. Predicting and Ranking Diabetic Ketoacidosis Risk among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model. Diabetes Technology & Therapeutics 2025 View
  8. Ganatra H. Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions. Journal of Clinical Medicine 2025;14(3):807 View
  9. Earnest A, Jones T, Chee M, Holmes-Walker D. Machine learning techniques to predict diabetic ketoacidosis and HbA1c above 7% among individuals with type 1 diabetes — A large multi-centre study in Australia and New Zealand. Nutrition, Metabolism and Cardiovascular Diseases 2025:103861 View