Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery

OBJECTIVE: Postoperative acute kidney injury (PO-AKI) is a common complication after cardiac surgery. We aimed to evaluate whether machine learning algorithms could significantly improve the risk prediction of PO-AKI. METHODS: The retrospective cohort study included 2310 adult patients undergoing ca...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Jicheng, Liu, Xinyun, Cheng, Zhaoyun, Liu, Qianjin, Xing, Wenlu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631004/
https://www.ncbi.nlm.nih.gov/pubmed/37936067
http://dx.doi.org/10.1186/s12882-023-03324-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631004/
https://www.ncbi.nlm.nih.gov/pubmed/37936067
http://dx.doi.org/10.1186/s12882-023-03324-w

Ejemplares similares