Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma
Abstract The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperat...
Guardado en:
Autores principales: | Yeonhee Lee, Jiwon Ryu, Min Woo Kang, Kyung Ha Seo, Jayoun Kim, Jungyo Suh, Yong Chul Kim, Dong Ki Kim, Kook-Hwan Oh, Kwon Wook Joo, Yon Su Kim, Chang Wook Jeong, Sang Chul Lee, Cheol Kwak, Sejoong Kim, Seung Seok Han |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/453c98a5ce9c472bbe11c7f343605b8a |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Machine learning model to predict hypotension after starting continuous renal replacement therapy
por: Min Woo Kang, et al.
Publicado: (2021) -
Causal linkage between adult height and kidney function: An integrated population-scale observational analysis and Mendelian randomization study.
por: Sehoon Park, et al.
Publicado: (2021) -
Nationwide Glaucoma incidence in end stage renal disease patients and kidney transplant recipients
por: Jong Joo Moon, et al.
Publicado: (2021) -
Prognostic significance of pathologic nodal positivity in non-metastatic patients with renal cell carcinoma who underwent radical or partial nephrectomy
por: Sung Han Kim, et al.
Publicado: (2021) -
Incident Parkinson’s disease in kidney transplantation recipients: a nationwide population-based cohort study in Korea
por: Seon Ha Baek, et al.
Publicado: (2021)