Post-treatment infection prediction in CLL using domain adaptation of lymphoma electronic health records
These findings highlight the value of integrating data from different diseases (lymphoma) to improve predictions in a target disease (CLL), and represent a step toward data-driven identification of CLL patients at high risk of infection during treatment.
Authors
- Christian Brieghel (ORCID: https://orcid.org/0000-0002-1816-8106)
- Thomas Lacoppidan (ORCID: https://orcid.org/0000-0002-6705-3153)
- Rudi Agius
- Mehdi Parviz (ORCID: https://orcid.org/0000-0003-0786-5915)
- Carsten Utoft Niemann (ORCID: https://orcid.org/0000-0001-9880-5242)
- Mikkel Werling
- Emelie Rotbain
Institutions
- University of Copenhagen (DK)
- Copenhagen University Hospital (DK)
- Rigshospitalet (DK)
- Danish Cancer Society (DK)
Publication Details
- Journal
- Acta Oncologica
- Published
- 2026-02-19
- DOI
- https://doi.org/10.2340/1651-226x.2026.44569
- Primary Topic
- Chronic Lymphocytic Leukemia Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- AstraZeneca
- Innovationsfonden