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 language | English |
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Title of host publication | Digital Health and Informatics Innovations for Sustainable Health Care Systems |
Number of pages | 2 |
Volume | 316 |
Publisher | IOS Press |
Publication date | 22 Aug 2024 |
Pages | 1759-1760 |
ISBN (Electronic) | 978-1-64368-533-5 |
DOIs | |
Publication status | Published - 22 Aug 2024 |
Event | 34th Medical Informatics Europe Conference (MIE): Digital Health & Informatics Innovations for Sustainable Health Care Systems - Athens, Greece Duration: 25 Aug 2024 → 29 Aug 2024 https://mie2024.org/ |
Conference
Conference | 34th Medical Informatics Europe Conference (MIE) |
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Country/Territory | Greece |
City | Athens |
Period | 25/08/2024 → 29/08/2024 |
Internet address |
Series | Studies in Health Technology and Informatics |
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Volume | 316 |
ISSN | 0926-9630 |
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