Machine learning model for predicting acute kidney injury progression in critically ill patients

被引:0
作者
Canzheng Wei
Lifan Zhang
Yunxia Feng
Aijia Ma
Yan Kang
机构
[1] West China Hospital of Sichuan University,Department of Critical Care Medicine
[2] West China Hospital of Sichuan University,Department of Gastroenterology
[3] University of Electronic Science and Technology of China,Department of Nephrology, Mianyan Central Hospital
来源
BMC Medical Informatics and Decision Making | / 22卷
关键词
Acute kidney injury; Critical care; Logistic Models; Extreme gradient boosting;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 235 条
[11]  
Amodeo S(2018)Comparison of variable selection methods for clinical predictive modeling Int J Med Inform 116 10-253
[12]  
Kimmel PL(2018)Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery J Clin Med 7 322-2830
[13]  
Palant CE(2016)MIMIC-III, a freely accessible critical care database Sci Data 3 160035-67
[14]  
Vanmassenhove J(2018)Predicting acute kidney injury: current status and future challenges J Nephrol 31 209-1103
[15]  
Kielstein J(2018)Feature ranking in predictive models for hospital-acquired acute kidney injury Sci Rep 8 17298-563
[16]  
Jorres A(2018)Modest impact of serial measurements of acute kidney injury biomarkers in an adult intensive care unit Nephron 139 243-29
[17]  
Van Biesen W(2013)Development and standardization of a furosemide stress test to predict the severity of acute kidney injury Crit Care 17 207-8
[18]  
Chen J-J(2011)Scikit-learn: machine learning in Python J Mach Learn Res 12 2825-760
[19]  
Chang C-H(2011)mice: multivariate imputation by chained equations in R J Stat Softw 45 1-1423
[20]  
Huang Y-T(2019)Active learning from imbalanced data: a solution of online weighted extreme learning machine IEEE Trans Neural Netw Learn Syst 30 1088-67