Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines
Unsupervised machine learning identified distinct phenogroups among pediatric cancer patients undergoing anthracycline chemotherapy, offering potential for personalized risk assessment.
Authors
- Shelby Kutty (ORCID: https://orcid.org/0000-0001-9428-0979)
- Benjamin Barnes (ORCID: https://orcid.org/0000-0001-8336-8756)
- Bhargava Chinni (ORCID: https://orcid.org/0000-0002-8889-1084)
- Xander Jacquemyn (ORCID: https://orcid.org/0000-0001-8421-0686)
- Cedric Manlhiot (ORCID: https://orcid.org/0000-0001-9419-7681)
- Sruti Rao (ORCID: https://orcid.org/0000-0003-3275-933X)
Institutions
- Johns Hopkins University (US)
- Universitair Ziekenhuis Leuven (BE)
- Johns Hopkins Hospital (US)
- KU Leuven (BE)
Publication Details
- Journal
- Cardio-Oncology
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1186/s40959-024-00276-4
- Citations
- 2
- Primary Topic
- Chemotherapy-induced cardiotoxicity and mitigation
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- Belgian American Educational Foundation