This article is a correction to:
Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide
Xiang Yu
RiLiGe Wu
YuWei Ji1
Zhe Feng- 1State Key Laboratory of Kidney Diseases, Department of Nephrology, Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Institute of Nephrology, National Clinical Research Center of Kidney Diseases, Beijing, China
- 2Medical Big Data Research Center, Chinese People's Liberation Army General Hospital, Beijing, China
A corrigendum on
Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide
by Yu, X., Wu, R., Ji, Y., and Feng, Z. (2023). Front. Public Health 11:1136939. doi: 10.3389/fpubh.2023.1136939
In the published article, there was an error.
“Regarding authors, Bihorac, A and Ozrazgat-Baslanti, T from the Kansas City Medical Center have published 10 articles.”
“Table 2 shows that Bihorac, A and Ozrazgat-Baslanti, T from the Kansas City Medical Center, USA, are the most productive authors.”
“In terms of author contributions, both prolific authors published 10 papers, while Bihorac, Azra and Ozrazgat, Baslanti, Tezcan, both from the University of Gainesville School of Medicine, USA, had the highest H-index and high total citations, and their research focused on the prediction of surgery-related AKI (15–17).”
The corrected sentence appears below:
“Regarding authors, Bihorac, A and Ozrazgat-Baslanti, T from the University of Florida have published 10 articles.”
“Table 2 shows that Bihorac, A and Ozrazgat-Baslanti, T from the University of Florida, USA, are the most productive authors.”
“In terms of author contributions, both prolific authors published 10 papers, while Bihorac, Azra and Ozrazgat, Baslanti, Tezcan, both from the University of Florida, USA, had the highest H-index and high total citations, and their research focused on the prediction of surgery-related AKI (15–17).”
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: machine learning, acute kidney injury, bibliometric analysis, model, critical care, hotspot
Citation: Yu X, Wu R, Ji Y and Feng Z (2024) Corrigendum: Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide. Front. Public Health 12:1430491. doi: 10.3389/fpubh.2024.1430491
Received: 10 May 2024; Accepted: 21 May 2024;
Published: 11 June 2024.
Approved by:
Frontiers Editorial Office, Frontiers Media SA, SwitzerlandCopyright © 2024 Yu, Wu, Ji and Feng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhe Feng, zhezhe_4025@126.com