Bibliometric analysis of 100 top cited articles of heart failure–associated diseases in combination with machine learning

Abstract

ObjectiveThe aim of this paper is to analyze the application of machine learning in heart failure-associated diseases using bibliometric methods and to provide a dynamic and longitudinal bibliometric analysis of heart failure–related machine learning publications.Materials and methodsWeb of Science was screened to gather the articles for the study. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility. Intuitive data analysis was employed to analyze the top-100 cited articles and VOSViewer was used to analyze the relevance and impact of all articles. The two analysis methods were then compared to get conclusions.ResultsThe search identified 3,312 articles. In the end, 2,392 papers were included in the study, which were published between 1985 and 2023. All articles were analyzed using VOSViewer. Key points of the analysis included the co-authorship map of authors, countries and organizations, the citation map of journal and documents and a visualization of keyword co-occurrence analysis. Among these 100 top-cited papers, with a mean of 122.9 citations, the most-cited article had 1,189, and the least cited article had 47. Harvard University and the University of California topped the list among all institutes with 10 papers each. More than one-ninth of the authors of these 100 top-cited papers wrote three or more articles. The 100 articles came from 49 journals. The articles were divided into seven areas according to the type of machine learning approach employed: Support Vector Machines, Convolutional Neural Networks, Logistic Regression, Recurrent Neural Networks, Random Forest, Naive Bayes, and Decision Tree. Support Vector Machines were the most popular method.ConclusionsThis analysis provides a comprehensive overview of the artificial intelligence (AI)-related research conducted in the field of heart failure, which helps healthcare institutions and researchers better understand the prospects of AI in heart failure and formulate more scientific and effective research plans. In addition, our bibliometric evaluation can assist healthcare institutions and researchers in determining the advantages, sustainability, risks, and potential impacts of AI technology in heart failure.

Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis

“…In this study, VOSviewer software Was used To calculate The frequency of keyword occurrence and Total link strength (TLS). TLS represents The total co-occurrence frequency of a keyword with other keywords; The larger The value, The greater The importance of The node In The network graph ( Kuang et al, 2023 ). Table 4 Presents information about The frequency of keyword occurrence and TLS.…”

“…They offer an opportunity to understand information within a research field and facilitate interdisciplinary collaboration. As a measurable information science method, bibliometrics analyze emerging trends and knowledge structures within a field to obtain quantifiable, repeatable, and objective data ( Ai et al, 2023 ; Kuang et al, 2023 ). Although some reviews have summarized the application of AI in the mental health of college students ( Abd-Alrazaq et al, 2024 ), bibliometric methods have not yet been applied to this research field.…”

Top 100 most-cited articles on tau protein: a bibliometric analysis and evidence mapping

“…The number of citations in an article serves as a valuable metric to assess the influence and significance it holds within the scientific community ( Shekhani et al, 2017 ). Citations provide researchers with a quantitative measure of how extensively their work has been referenced by other scholars in their own studies, indicating its impact on advancing knowledge and shaping future research directions ( Merigó and Núñez, 2016 ; Huang et al, 2022 ; Kuang et al, 2023 ).…”