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Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization
BACKGROUND: Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI pa...
Autores principales: | Zhou, Hongshan, Liu, Leping, Zhao, Qinyu, Jin, Xin, Peng, Zhangzhe, Wang, Wei, Huang, Ling, Xie, Yanyun, Xu, Hui, Tao, Lijian, Xiao, Xiangcheng, Nie, Wannian, Liu, Fang, Li, Li, Yuan, Qiongjing |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10106833/ https://www.ncbi.nlm.nih.gov/pubmed/37077912 http://dx.doi.org/10.3389/fimmu.2023.1140755 |
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