Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care-Reference-Cited by-同舟云学术

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

Published:2019-04-08 Issue:1 Volume:23 Page:
ISSN:1364-8535
Container-title:Critical Care
language:en
Short-container-title:Crit Care

Author:

Zhang Zhongheng^ORCID,Ho Kwok M.,Hong Yucai

Publisher

Springer Science and Business Media LLC

Subject

Critical Care and Intensive Care Medicine

Link

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

Reference27 articles.

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2. Anseeuw K, Mowry JB, Burdmann EA, Ghannoum M, Hoffman RS, Gosselin S, et al. Extracorporeal treatment in phenytoin poisoning: systematic review and recommendations from the EXTRIP (Extracorporeal Treatments in Poisoning) Workgroup. Am J Kidney Dis. 2016;67:187–97.

3. Ostermann M, Chang RWS. Acute kidney injury in the intensive care unit according to RIFLE. Crit Care Med. 2007;35:1837–43–quiz1852.

4. Kidney Disease Improving Global Outcomes KDIGO Acute Kidney Injury Work Group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int. 2012;suppl 2:1–138.

5. Miller TE, Bunke M, Nisbet P, Brudney CS. Fluid resuscitation practice patterns in intensive care units of the USA: a cross-sectional survey of critical care physicians. Perioper Med (Lond) BioMed Central. 2016;5:15.

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