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SUMMARY:Machine Learning in HIV Testing: A Bibliometric Analysis of Publis
 hed Studies 2000-2024
DTSTART;VALUE=DATE-TIME:20240703T093500Z
DTEND;VALUE=DATE-TIME:20240703T095500Z
DTSTAMP;VALUE=DATE-TIME:20260310T190038Z
UID:indico-contribution-2025@events.chpc.ac.za
DESCRIPTION:Speakers: Musa Jaiteh (University of Johannesburg)\nBackground
 : The human deficiency virus (HIV) remains a devastating public health thr
 eat\, affecting 39 million people globally\, with approximately 60% of the
 se cases occurring in Sub-Saharan Africa. Early detection and diagnosis of
  HIV are crucial for preventing the further spread of the virus\, making H
 IV testing a pivotal tool for achieving the UNAIDS goal of ending AIDS by 
 2030. The World Health Organization and UNAIDS have emphasized the importa
 nce of adopting innovative testing strategies\, such as those involving ma
 chine learning. Machine learning can accurately predict high-risk individu
 als and facilitate more effective and efficient testing methods compared t
 o traditional approaches. Despite this advancement\, there exists a knowle
 dge gap regarding the extent to which machine learning techniques are inte
 grated into HIV testing strategies worldwide. To address this gap\, this s
 tudy aimed to analyze published studies that applied machine learning to H
 IV from 2000 to 2024.\nMethods: This study utilized a bibliometric approac
 h to analyze studies that were focused on the use of machine learning in H
 IV testing.  Relevant studies were captured through the Web of Science dat
 abase using synonymous keywords. The bibliometrics package in R was used t
 o analyze the characteristics\, citation patterns\, and contents of 3962 a
 rticles\, while VOSviewer was used to conduct network violations. The anal
 ysis focused on the yearly growth rate\, citation analysis\, keywords\, in
 stitutions\, countries\, authorship\, and collaboration patterns. \nResult
 s: The analysis revealed a scientific annual growth rate of 8.8% with an i
 nternational co-authorship of 44.7% and an average citation of 23.16 per d
 ocument. The most relevant sources were from high-impact journals such as 
 PLOS ONE\, Aids and Behavior\, Journal of Acquired Immune Deficiency Syndr
 ome\, Journal of International Aids Society\, AIDs\, and BMC Public Health
 .  The USA\, The United Kingdom\, South Africa\, China\, and Canada produc
 e the highest number of contributions. The results show that the Universit
 y of California\, Johns Hopkins University\, Harvard University\, and the 
 University of London have the highest collaboration networks.\nConclusion:
  This study identifies trends and hotspots of machine learning research re
 lated to HIV testing across various countries\, institutions\, journals\, 
 and authors. These insights are crucial for future researchers to understa
 nd the dynamics of research outputs in this field.\n\nhttps://events.chpc.
 ac.za/event/134/contributions/2025/
LOCATION:CSIR ICC ICC-G-Ruby - Ruby Auditorium
URL:https://events.chpc.ac.za/event/134/contributions/2025/
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