Accepted for/Published in: Interactive Journal of Medical Research
Date Submitted: Jul 28, 2024
Date Accepted: Dec 12, 2024
Application of Machine Learning and Emerging Health Technologies in the Uptake of HIV Testing: A Bibliometric Analysis of Published Studies 2000-2024
ABSTRACT
Background:
The global targets for human immunodeficiency virus (HIV) testing for achieving the United Nations Joint Programme of HIV/AIDS (UNAIDS) 95–95-95 are still short. Identifying factors associated with HIV testing and improving its uptake is crucial in fast-tracking the second (initiate people living with HIV (PLHIV) on antiretroviral therapy) and third (viral suppression) UNAIDS goals. Machine learning can precisely predict high-risk individuals and facilitate more effective and efficient HIV testing methods. Despite this advancement, there exists a research gap regarding the extent to which machine learning techniques are integrated into HIV testing strategies worldwide.
Objective:
The study aimed to examine the characteristics, citation patterns, and contents of published studies applying machine learning in HIV testing from 2000 to 2024.
Methods:
This bibliometric analysis identified relevant studies utilizing machine learning in HIV testing from the Web of Science database using synonymous keywords. The Bibliometrix R package version 4.3.2 was used to analyze the characteristics, citation patterns, and contents of 649 articles, while VOSviewer version 1.6.20 was used to conduct network visualization. The analysis focused on the yearly growth rate, citation analysis, keywords, institutions, countries, authorship, and collaboration patterns. Key themes and topics were driven by the authors’ most frequent keywords, which aided the content analysis.
Results:
The analysis revealed a scientific annual growth rate of 5.25%, with an international co-authorship of 32.05% and an average citation of 20.35 per document. The most relevant sources were from high-impact journals such as the Journal of AIDS and Behavior, AIDS Education and Prevention, Journal of Medical Internet Research Journal of AIDS, and PLOS ONE. The United States, The United Kingdom, South Africa, Canada, and China produced the highest number of contributions. Collaboration analysis showed networks among the first-world universities, including the University of North Carolina, Emory University, Johns Hopkins University, the University of Pennsylvania, the University of California San Fransisco, and the London School of Hygiene and Tropical Medicine, highlighting missed opportunities in strategic partnerships between the developed and developing countries. The results further demonstrate that machine learning enhances the effective and efficient implementation of innovative HIV testing methods, including HIV self-testing among priority populations.
Conclusions:
This study identifies trends and hotspots of machine learning research in relation to HIV testing across various countries, institutions, journals, and authors. These insights are crucial for future researchers to understand the dynamics of research outputs to make scholarly decisions in addressing research gaps in this field. Clinical Trial: Not applicable
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Copyright
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