Cargando…
Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care
BACKGROUND AND OBJECTIVES: Excess fluid balance in acute kidney injury (AKI) may be harmful, and conversely, some patients may respond to fluid challenges. This study aimed to develop a prediction model that can be used to differentiate between volume-responsive (VR) and volume-unresponsive (VU) AKI...
Autores principales: | Zhang, Zhongheng, Ho, Kwok M., Hong, Yucai |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454725/ https://www.ncbi.nlm.nih.gov/pubmed/30961662 http://dx.doi.org/10.1186/s13054-019-2411-z |
Ejemplares similares
-
Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury
por: Jiang, Meng, et al.
Publicado: (2023) -
Early-Phase Urine Output and Severe-Stage Progression of Oliguric Acute Kidney Injury in Critical Care
por: Huang, Haoquan, et al.
Publicado: (2021) -
COVID‐19 patients in intensive care develop predominantly oliguric acute kidney injury
por: Luther, Tomas, et al.
Publicado: (2020) -
Pseudomyxoma Peritonei: A Rare Cause of Oliguric Acute Kidney Injury
por: Min, Hye Sook, et al.
Publicado: (2013) -
Cisplatin-Induced Non-Oliguric Acute Kidney Injury in a Pediatric Experimental Animal Model in Piglets
por: Santiago, Maria José, et al.
Publicado: (2016)