A Machine Learning Approach for the Prediction of Severe Acute Kidney Injury Following Traumatic Brain Injury
Author:
Publisher
Springer Science and Business Media LLC
Subject
Critical Care and Intensive Care Medicine,Neurology (clinical)
Link
https://link.springer.com/content/pdf/10.1007/s12028-022-01606-z.pdf
Reference33 articles.
1. Kolias AG, Rubiano AM, Figaji A, Servadei F, Hutchinson PJ. Traumatic brain injury: global collaboration for a global challenge. Lancet Neurol. 2019;18(2):136–7. https://doi.org/10.1016/s1474-4422(18)30494-0.
2. Goyal K, Hazarika A, Khandelwal A, et al. Non- neurological complications after traumatic brain injury: a prospective observational study. Indian J Crit Care Med Peer-Rev Off Publ Indian Soc Crit Care Med. 2018;22(9):632–8. https://doi.org/10.4103/ijccm.IJCCM_156_18.
3. Moore EM, Bellomo R, Nichol A, Harley N, Macisaac C, Cooper DJ. The incidence of acute kidney injury in patients with traumatic brain injury. Ren Fail. 2010;32(9):1060–5. https://doi.org/10.3109/0886022x.2010.510234.
4. Li N, Zhao WG, Zhang WF. Acute kidney injury in patients with severe traumatic brain injury: implementation of the acute kidney injury network stage system. Neurocrit Care. 2011;14(3):377–81. https://doi.org/10.1007/s12028-011-9511-1.
5. Li N, Zhao WG, Xu FL, Zhang WF, Gu WT. Neutrophil gelatinase-associated lipocalin as an early marker of acute kidney injury in patients with traumatic brain injury. J Nephrol. 2013;26(6):1083–8. https://doi.org/10.5301/jn.5000282.
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