Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care

Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care

Abstract 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...

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Bibliographic Details
Main Authors: Zhongheng Zhang, Kwok M. Ho, Yucai Hong
Format: Article
Language:English
Published: BMC 2019-04-01
Series:Critical Care
Subjects:
Acute kidney injury
Critical care
Extreme gradient boosting
Urine output
Predictive modeling
Online Access:http://link.springer.com/article/10.1186/s13054-019-2411-z

Internet

http://link.springer.com/article/10.1186/s13054-019-2411-z

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