Accepted for/Published in: Interactive Journal of Medical Research
Date Submitted: Nov 11, 2023
Date Accepted: Mar 4, 2024
Application of Artificial Intelligence in Sepsis: Citation Network Analysis and Evidence Synthesis
ABSTRACT
Background:
Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field, and identify current research trends, and future research directions.
Objective:
The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis.
Methods:
A search was conducted in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was employed, evaluating titles, abstracts, and full texts as needed. The bibliometrix and VOSviewer tools were utilized to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords.
Results:
A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publications count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.56%), Frontiers in Medicine (17/259, 6.56%), and Scientific Reports (11/259, 4.24%). The United States (103/259, 39.76%), China (83/259, 32.04%), England (14/259, 5.40%), and Taiwan (12/259, 4.63%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Das R., Barton C., and Kamaleswaran R. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023).
Conclusions:
This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding healthcare policymakers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis. Clinical Trial: N/A
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.