AKIMLpred: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study-Reference-Cited by-同舟云学术

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AKIMLpred: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study

Published:2024-06 Issue: Volume:559 Page:119705
ISSN:0009-8981
Container-title:Clinica Chimica Acta
language:en
Short-container-title:Clinica Chimica Acta

Author:

Sun Tao,Yue Xiaofang,Zhang Gong,Lin Qinyan,Chen Xiao,Huang Tiancha,Li Xiang,Liu Weiwei,Tao Zhihua

Publisher

Elsevier BV

Reference35 articles.

1. Acute kidney injury;Kellum;Nat. Rev. Dis. Primers,2021

2. Acute kidney injury;Ronco;Lancet,2019

3. Paradigms of acute kidney injury in the intensive care setting;Kellum;Nat. Rev. Nephrol.,2018

4. Acute kidney injury in the critically ill: an updated review on pathophysiology and management;Pickkers;Intensive Care Med.,2021

5. Association between urinary dickkopf-3, acute kidney injury, and subsequent loss of kidney function in patients undergoing cardiac surgery: an observational cohort study;Schunk;Lancet,2019

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