Machine Learning‐Based Detection of HbS and HbC Carriers in the UK General Population
ML models based on routine blood tests effectively identify HbS and HbC carriers in a mixed general population. This approach has the potential to enhance screening efficiency by reducing reliance on specialised techniques.
Authors
- Deniz Kenan Kılıç (ORCID: https://orcid.org/0000-0002-6996-3425)
- Tarec Christoffer El‐Galaly (ORCID: https://orcid.org/0000-0002-4406-380X)
- Jesper Petersen (ORCID: https://orcid.org/0000-0001-6116-5114)
- Izabela Nielsen (ORCID: https://orcid.org/0000-0002-3506-2741)
- Jens Helby (ORCID: https://orcid.org/0000-0002-1876-8187)
- Andreas Glenthøj (ORCID: https://orcid.org/0000-0003-2082-0738)
- Alexander Djupnes Fuglkjær (ORCID: https://orcid.org/0009-0000-3879-1950)
- Frederik Christensen (ORCID: https://orcid.org/0009-0002-2410-8852)
Institutions
- University of Copenhagen (DK)
- Aarhus University (DK)
- Aarhus University Hospital (DK)
- Copenhagen University Hospital (DK)
- Psychiatric Association of Turkey (TR)
- Aalborg University (DK)
Publication Details
- Journal
- eJHaem
- Published
- 2025-11-04
- DOI
- https://doi.org/10.1002/jha2.70170
- Primary Topic
- Hemoglobinopathies and Related Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Novo Nordisk
- Novo Nordisk Fonden