Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide

Frontiers in Public Health

Public Health

RiLiGe Wu¹  Xiang Yu²  YuWei Ji²  Zhe Feng² 

[1]Medical Big Data Research Center, Chinese People's Liberation Army General Hospital, Beijing, China

[2]State 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

关键词: machine learning;    acute kidney injury;    bibliometric analysis;    model;    critical care;    hotspot;   

DOI  :  10.3389/fpubh.2023.1136939

 received in 2023-01-03, accepted in 2023-03-01,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundAcute kidney injury (AKI) is a serious clinical complication associated with adverse short-term and long-term outcomes. In recent years, with the rapid popularization of electronic health records and artificial intelligence machine learning technology, the detection rate and treatment of AKI have been greatly improved. At present, there are many studies in this field, and a large number of articles have been published, but we do not know much about the quality of research production in this field, as well as the focus and trend of current research.MethodsBased on the Web of Science Core Collection, studies reporting machine learning-based AKI research that were published from 2013 to 2022 were retrieved and collected after manual review. VOSviewer and other software were used for bibliometric visualization analysis, including publication trends, geographical distribution characteristics, journal distribution characteristics, author contributions, citations, funding source characteristics, and keyword clustering.ResultsA total of 336 documents were analyzed. Since 2018, publications and citations have increased dramatically, with the United States (143) and China (101) as the main contributors. Regarding authors, Bihorac, A and Ozrazgat-Baslanti, T from the Kansas City Medical Center have published 10 articles. Regarding institutions, the University of California (18) had the most publications. Approximately 1/3 of the publications were published in Q1 and Q2 journals, of which Scientific Reports (19) was the most prolific journal. Tomašev et al.'s study that was published in 2019 has been widely cited by researchers. The results of cluster analysis of co-occurrence keywords suggest that the construction of AKI prediction model related to critical patients and sepsis patients is the research frontier, and XGBoost algorithm is also popular.ConclusionThis study first provides an updated perspective on machine learning-based AKI research, which may be beneficial for subsequent researchers to choose suitable journals and collaborators and may provide a more convenient and in-depth understanding of the research basis, hotspots and frontiers.

【 授权许可】

Unknown   
Copyright © 2023 Yu, Wu, Ji and Feng.