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
Machine learning models can be effectively implemented on real-life large clinical datasets and they perform well in terms of identifying individuals with type 1 diabetes at risk of adverse outcomes.
Authors
- Timothy W. Jones (ORCID: https://orcid.org/0000-0002-7989-1998)
- Arul Earnest (ORCID: https://orcid.org/0000-0003-2693-5034)
- Melissa Chee
- D. Jane Holmes–Walker (ORCID: https://orcid.org/0000-0002-3640-1370)
Institutions
- The Kids Research Institute Australia (AU)
- Westmead Hospital (AU)
- Monash Health (AU)
- Juvenile Diabetes Research Foundation (GB)
- Monash University (AU)
Publication Details
- Journal
- Nutrition Metabolism and Cardiovascular Diseases
- Published
- 2025-01-09
- DOI
- https://doi.org/10.1016/j.numecd.2025.103861
- Citations
- 2
- Primary Topic
- Diabetes and associated disorders
- Type
- article
- Field-Weighted Citation Impact
- 3.63