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Paper Details

Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records.
JAMA Netw Open
12
2022
AKI, Acute Kidney Injury, Acute kidney injury, Kidney Disease, Patients, cardiac, creatinine, kidney dysfunction, men, patient, patients
Acute Kidney Injury, Adult, Area Under Curve, Creatinine, Electronic Health Records, Humans, Machine Learning, Male, Middle Aged
Author NameAffiliation
Kang LiuBig Data Decision Institute, Jinan University
Xiangzhou ZhangBig Data Decision Institute, Jinan University
Weiqi ChenBig Data Decision Institute, Jinan University
Alan S L YuDivision of Nephrology and Hypertension and the Jared Grantham Kidney Institute, University of Kansas Medical Center
John A KellumCenter for Critical Care Nephrology, University of Pittsburgh School of Medicine
Michael MathenyVanderbilt University School of Medicine
Michael E MathenyGeriatrics Research Education and Clinical Care Center, Veterans Affairs Tennessee Valley Healthcare System
Michael E MathenyVanderbilt University School of Medicine
Michael E MathenyVanderbilt University School of Medicine
Michael E MathenyVanderbilt University School of Medicine
Michael MathenyVanderbilt University School of Medicine
Michael MathenyVanderbilt University School of Medicine
Michael MathenyGeriatrics Research Education and Clinical Care Center, Veterans Affairs Tennessee Valley Healthcare System
Steven Q SimpsonUniversity of Kansas Medical Center
Yong HuBig Data Decision Institute, Jinan University
Mei LiuUniversity of Kansas Medical Center
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Release 1.2