Comparison of Prediction Models for Acute Kidney Injury Among Patients with Hepatobiliary Malignancies Based on XGBoost and LASSO-Logistic Algorithms

BACKGROUND: Based on the admission data, we applied the XGBoost algorithm to create a prediction model to estimate the AKI risk in patients with hepatobiliary malignancies and then compare its prediction capacity with the logistic model. METHODS: We reviewed clinical data of 7968 and 589 liver/gallb...

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Detalles Bibliográficos
Autores principales: Zhang, Yunlu, Wang, Yimei, Xu, Jiarui, Zhu, Bowen, Chen, Xiaohong, Ding, Xiaoqiang, Li, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057825/
https://www.ncbi.nlm.nih.gov/pubmed/33889012
http://dx.doi.org/10.2147/IJGM.S302795

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057825/
https://www.ncbi.nlm.nih.gov/pubmed/33889012
http://dx.doi.org/10.2147/IJGM.S302795

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