Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia

Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia

Abstract The prediction of relapse in childhood acute lymphoblastic leukemia (ALL) is a critical factor for successful treatment and follow-up planning. Our goal was to construct an ALL relapse prediction model based on machine learning algorithms. Monte Carlo cross-validation nested by 10-fold cros...

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Autores principales: Liyan Pan, Guangjian Liu, Fangqin Lin, Shuling Zhong, Huimin Xia, Xin Sun, Huiying Liang
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2017
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/dea91707cbc6485f9137aa31ade2002e
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