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

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 649...

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Bibliographic Details
Main Authors: Yang Li, Xiaohong Chen, Ziyan Shen, Yimei Wang, Jiachang Hu, Yunlu Zhang, Jiarui Xu, Xiaoqiang Ding
Format: Article
Language:English
Published: Taylor & Francis Group 2020-01-01
Series:Renal Failure
Subjects:
gastrointestinal cancer
acute kidney injury
bayesian network
group lasso
disease prediction
machine learning
Online Access:http://dx.doi.org/10.1080/0886022X.2020.1810068

Internet

http://dx.doi.org/10.1080/0886022X.2020.1810068

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