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

Digital Health Reviews (1724) Quality Improvement (294) Machine Learning (2076) Artificial Intelligence (2129) Development and Evaluation of Research Methods, Instruments and Tools (744) Adoption and Change Management of eHealth Systems (894) Digital Health Reporting Standards, Quality and Transparency in e-Research (132)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47430, first published .
The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review

The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review

The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review

Authors of this article:

Zsombor Zrubka1 Author Orcid Image ;   Gábor Kertész2 Author Orcid Image ;   László Gulácsi1 Author Orcid Image ;   János Czere3 Author Orcid Image ;   Áron Hölgyesi1, 4 Author Orcid Image ;   Hossein Motahari Nezhad1, 5 Author Orcid Image ;   Amir Mosavi2 Author Orcid Image ;   Levente Kovács6 Author Orcid Image ;   Atul J Butte7 Author Orcid Image ;   Márta Péntek1 Author Orcid Image
Article Authors Cited by (5) Tweetations (3) 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. Hamza Yousif B, Alsadig Abdalwahab Abdallah A, Ibrahim Abdelhalim A, Mohammedosman M, Hafez Sadaka S, Abdelaziz Alzobeir S. Transparency and Validity of Artificial Intelligence Applications in Pediatric Diabetes: A Systematic Review. Cureus 2025 View

Conference Proceedings

  1. Alabdulkarim H, Zrubka Z. 2024 IEEE 28th International Conference on Intelligent Engineering Systems (INES). How Effective is Continuous Glucose Monitoring? Cumulative Meta-regression Analysis View
  2. Fgaier M, Zrubka Z. 2024 IEEE 28th International Conference on Intelligent Engineering Systems (INES). A scoping review of evaluation and acceptability criteria for the accuracy of cost estimates in health economic evaluations View
  3. Fgaier M, Zrubka Z, Alabdulkarim H. 2025 IEEE 19th International Symposium on Applied Computational Intelligence and Informatics (SACI). Feasibility of Health Technology Assessment in the Mena Region Using Transferred Costs: Analysing the Feasibility of Four Imputation Strategies of Published Costs View