A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection

OBJECTIVE: Machine learning methods may have better or comparable predictive ability than traditional analysis. We explore machine learning methods to predict the likelihood of acute kidney injury after liver cancer resection. METHODS: This is a secondary analysis cohort study. We reviewed data from...

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Detalles Bibliográficos
Autores principales: Lei, Lei, Wang, Ying, Xue, Qiong, Tong, Jianhua, Zhou, Cheng-Mao, Yang, Jian-Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Bioinformatics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7047869/
https://www.ncbi.nlm.nih.gov/pubmed/32140301
http://dx.doi.org/10.7717/peerj.8583

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7047869/
https://www.ncbi.nlm.nih.gov/pubmed/32140301
http://dx.doi.org/10.7717/peerj.8583

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