Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records-Reference-Cited by-同舟云学术

Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records

Published:2022-07-01 Issue:7 Volume:5 Page:e2219776
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Liu Kang¹,Zhang Xiangzhou¹,Chen Weiqi¹,Yu Alan S. L.²,Kellum John A.³,Matheny Michael E.⁴⁵⁶⁷,Simpson Steven Q.⁸,Hu Yong¹,Liu Mei⁹

Affiliation:

1. Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China

2. Division of Nephrology and Hypertension and the Jared Grantham Kidney Institute, School of Medicine, University of Kansas Medical Center, Kansas City

3. Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania

4. Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee

5. Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee

6. Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee

7. Geriatrics Research Education and Clinical Care Center, Veterans Affairs Tennessee Valley Healthcare System, Nashville

8. Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, University of Kansas Medical Center, Kansas City

9. Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2793807/liu_2022_oi_220569_1661363011.94703.pdf

Reference81 articles.

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

2. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study.;Hoste;Intensive Care Med,2015

3. Incidence, outcomes, and comparisons across definitions of AKI in hospitalized individuals.;Zeng;Clin J Am Soc Nephrol,2014

4. Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations.;Hodgson;BMJ Open,2017

5. A clinically applicable approach to continuous prediction of future acute kidney injury.;Tomašev;Nature,2019

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