Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines ; volume:10 ; number:1 ; day:28 ; month:10 ; year:2024 ; pages:1-11 ; date:12.2024
Cardio-Oncology ; 10, Heft 1 (28.10.2024), 1-11, 12.2024

Creator: Jacquemyn, Xander
Chinni, Bhargava K.
Barnes, Benjamin T.
Rao, Sruti
Kutty, Shelby
Manlhiot, Cedric

Contributor: SpringerLink (Online service)

DOI: 10.1186/s40959-024-00276-4

URN: urn:nbn:de:101:1-2501172141118.327920973854

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:34 AM CEST

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Jacquemyn, Xander
Chinni, Bhargava K.
Barnes, Benjamin T.
Rao, Sruti
Kutty, Shelby
Manlhiot, Cedric
SpringerLink (Online service)

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Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines

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