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
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Acceso en línea:https://doaj.org/article/dea91707cbc6485f9137aa31ade2002e
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