Prediction models for acute kidney injury in patients with gastrointestinal cancers: a real-world study based on Bayesian networks

BACKGROUND: This study attempts to establish a Bayesian networks (BNs) based model for inferring the risk of AKI in gastrointestinal cancer (GI) patients, and to compare its predictive capacity with other machine learning (ML) models. METHODS: From 1 October 2014 to 30 September 2015, we recruited 6...

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Detalles Bibliográficos
Autores principales: Li, Yang, Chen, Xiaohong, Shen, Ziyan, Wang, Yimei, Hu, Jiachang, Zhang, Yunlu, Xu, Jiarui, Ding, Xiaoqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2020
Materias:
Clinical Study
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472473/
https://www.ncbi.nlm.nih.gov/pubmed/32838613
http://dx.doi.org/10.1080/0886022X.2020.1810068

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472473/
https://www.ncbi.nlm.nih.gov/pubmed/32838613
http://dx.doi.org/10.1080/0886022X.2020.1810068

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