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Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors

Crossref DOI link: https://doi.org/10.1016/j.jaccao.2020.11.004

Published Print: 2020-12

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Chaix, Marie-A https://orcid.org/0000-0002-7748-2779
Parmar, Neha

Kinnear, Caroline

Lafreniere-Roula, Myriam

Akinrinade, Oyediran

Yao, Roderick

Miron, Anastasia

Lam, Emily

Meng, Guoliang

Christie, Anne

Manickaraj, Ashok Kumar

Marjerrison, Stacey

Dillenburg, Rejane

Bassal, Mylène

Lougheed, Jane

Zelcer, Shayna

Rosenberg, Herschel

Hodgson, David

Sender, Leonard

Kantor, Paul

Manlhiot, Cedric

Ellis, James

Mertens, Luc

Nathan, Paul C.

Mital, Seema https://orcid.org/0000-0002-7643-4484
Funding

Funding for this research was provided by:

Pediatric Oncology Group of Ontario

C17 Children's Cancer and Blood Disorders

Canadian Institutes of Health Research

Canadian Cancer Society

Ontario Institute for Cancer Research
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Text and Data Mining valid from 2020-12-01

Text and Data Mining valid from 2020-12-01

Version of Record valid from 2020-11-05
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This article is maintained by: Elsevier

Article Title: Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors

Journal Title: JACC: CardioOncology

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jaccao.2020.11.004

Content Type: article

Copyright: © 2020 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.