Prediction of Poor Glycemic Control in Children with Type 1 Diabetes

Tanja F Holm*, Morten H Jensen, Ole K Hejlesen, Søren Hagstrøm, Mette Madsen, Stine Hangaard

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingBook chapterResearchpeer-review

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Abstract

This study developed and validated a machine learning model for predicting glycemic control in children with type 1 diabetes at the time of diagnosis, revealing age at diagnosis as the most informative predictor.

Original languageEnglish
Title of host publicationDigital Health and Informatics Innovations for Sustainable Health Care Systems
Number of pages2
Volume316
PublisherIOS Press
Publication date22 Aug 2024
Pages1759-1760
ISBN (Electronic)978-1-64368-533-5
DOIs
Publication statusPublished - 22 Aug 2024
Event34th Medical Informatics Europe Conference (MIE): Digital Health & Informatics Innovations for Sustainable Health Care Systems - Athens, Greece
Duration: 25 Aug 202429 Aug 2024
https://mie2024.org/

Conference

Conference34th Medical Informatics Europe Conference (MIE)
Country/TerritoryGreece
CityAthens
Period25/08/202429/08/2024
Internet address
SeriesStudies in Health Technology and Informatics
Volume316
ISSN0926-9630

Keywords

  • Adolescent
  • Blood Glucose
  • Child
  • Child, Preschool
  • Diabetes Mellitus, Type 1/blood
  • Female
  • Glycated Hemoglobin/analysis
  • Glycemic Control
  • Humans
  • Machine Learning
  • Male
  • glycemic control
  • decision support
  • children
  • Type 1 diabetes

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