Emergence and evolution of big data science in HIV research: Bibliometric analysis of federally sponsored studies 2000-2019

Application of Machine Learning and Emerging Health Technologies in the Uptake of HIV Testing: Bibliometric Analysis of Studies Published From 2000 to 2024

Background

The global targets for HIV testing for achieving the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets are still short. Identifying gaps and opportunities for HIV testing uptake is crucial in fast-tracking the second (initiate people living with HIV on antiretroviral therapy) and third (viral suppression) UNAIDS goals. Machine learning and health technologies 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 such technologies 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 and emerging health technologies in HIV testing from 2000 to 2024.

Methods

This bibliometric analysis identified relevant studies using machine learning and emerging health technologies in HIV testing from the Web of Science database using synonymous keywords. The Bibliometrix R package was used to analyze the characteristics, citation patterns, and contents of 266 articles. The VOSviewer software 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 15.68%, with an international coauthorship of 8.22% and an average citation count of 17.47 per document. The most relevant sources were from high-impact journals such as the Journal of Internet Medicine Research, JMIR mHealth and uHealth, JMIR Research Protocols, mHealth, AIDS Care-Psychological and Socio-Medical Aspects of AI, and BMC Public Health, and PLOS One. The United States of America, China, South Africa, the United Kingdom, and Australia produced the highest number of contributions. Collaboration analysis showed significant networks among universities in high-income countries, including the University of North Carolina, Emory University, the University of Michigan, San Diego State University, the University of Pennsylvania, and the London School of Hygiene and Tropical Medicine. The discrepancy highlights missed opportunities in strategic partnerships between high-income and low-income countries. The results further demonstrate that machine learning and health technologies enhance 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 and health technology research in relation to HIV testing across various countries, institutions, journals, and authors. The trends are higher in high-income countries with a greater focus on technology applications for HIV self-testing among young people and priority populations. These insights will inform future researchers about the dynamics of research outputs and help them make scholarly decisions to address research gaps in this field.

Mapping the intersection of HIV and Alzheimer's disease: a bibliometric analysis of emerging research trends

Background

HIV and Alzheimer's disease (AD) are significant global health challenges with overlapping neuroinflammatory and protein aggregation mechanisms. Understanding their intersection is critical for advancing therapeutic strategies, particularly in aging populations.

Objective

This study aims to provide a comprehensive bibliometric analysis of research trends at the intersection of HIV and AD, identify emerging themes, and highlight key contributors in this interdisciplinary field.

Methods

Using the Web of Science Core Collection, we retrieved 4,856 articles and reviews published between 1994 and 2025. Bibliometric analysis was conducted with VOSviewer, CiteSpace, and R software to examine publication trends, international collaboration, institutional contributions, journal dynamics, author networks, and thematic evolution.

Results

The analysis reveals a 14.18% annual growth rate in publications, with the U.S. leading in productivity, followed by China, Germany, and Japan. Key institutions include the NIH and the University of California System, while journals such as Journal of Biological Chemistry and PLOS ONE show significant growth. Prominent authors include Masliah, Eliezer, and Heaton, RK. Research highlights the overlap between HIV-associated neurocognitive disorders (HAND) and AD, emphasizing shared mechanisms like neuroinflammation, protein aggregation, and blood-brain barrier disruption. Recent advances focus on cerebrospinal fluid biomarkers, oxidative stress, and the impact of antiretroviral therapy (ART) on neurological outcomes. Studies increasingly explore the role of advanced methodologies, including machine learning, in elucidating shared mechanisms such as neuroinflammation, endoplasmic reticulum stress, and protein misfolding.

Conclusion

This bibliometric analysis underscores the dynamic and rapidly evolving research landscape at the intersection of HIV and AD, driven by collaborative efforts and technological advancements. Future research should prioritize longitudinal studies, mechanistic insights, and translational applications to address unanswered questions in this critical field.

Transparent exopolymer particles (TEP) research: From the ocean to water treatment

Transparent Exopolymer Particles (TEP), closely related to the carbon cycle due to their high carbon-to-nitrogen ratio, have become a hot research topic. However, despite the growing interest in this field, there is a lack of comprehensive analysis providing a clear developmental background and quantitative research on the overall trends of TEP studies. To address the gap, this article utilizes VOSviewer and CiteSpace to conduct a bibliometric analysis of TEP research. The results show that the largest contribution of publications in this field is in the United States and it has become an increasingly interdisciplinary research topic. We identify that the current research focus of TEP is mainly on the application of membrane fouling, marine microbial and biogeochemical cycle. In addition, the research focus has shifted from the role of TEP in the marine carbon cycle to its influence on membrane fouling. This study systematically and comprehensively carries out quantitative visualization and analysis of TEP, providing a basis and reference for in-depth understanding of the current status of TEP research as well as grasping the development trend.

A bibliometric review of predictive modelling for cervical cancer risk

Cervical cancer represents a significant public health challenge, particularly affecting women's health globally. This study aims to advance the understanding of cervical cancer risk prediction research through a bibliometric analysis. The study identified 800 records from Scopus and Web of Science databases, which were reduced to 142 unique records after removing duplicates. Out of 100 abstracts assessed, 42 were excluded based on specific criteria, resulting in 58 studies included in the bibliometric review. Multiple scoping methods such as thematic analysis, citation analysis, bibliographic coupling, natural language processing, Latent Dirichlet Allocation and other visualisation techniques were used to analyse related publications between 2013 and 2024. The key findings revealed the importance of interdisciplinary collaboration in cervical cancer risk prediction, integrating expertise from mathematical disciplines, biomedical health, healthcare practitioners, public health, and policy. This approach significantly enhanced the accuracy and efficiency of cervical cancer detection and predictive modelling by adopting advanced machine learning algorithms, such as random forests and support vector machines. The main challenges were the lack of external validation on independent datasets and the need to address model interpretability to ensure healthcare providers understand and trust the predictive models. The study revealed the importance of interdisciplinary collaboration in cervical cancer risk prediction. It made recommendations for future research to focus on increasing the external validation of models, improving model interpretability, and promoting global research collaborations to enhance the comprehensiveness and applicability of cervical cancer risk prediction models.

Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response

PURPOSE OF REVIEW

Big Data Science can be used to pragmatically guide the allocation of resources within the context of national HIV programs and inform priorities for intervention. In this review, we discuss the importance of grounding Big Data Science in the principles of equity and social justice to optimize the efficiency and effectiveness of the global HIV response.

RECENT FINDINGS

Social, ethical, and legal considerations of Big Data Science have been identified in the context of HIV research. However, efforts to mitigate these challenges have been limited. Consequences include disciplinary silos within the field of HIV, a lack of meaningful engagement and ownership with and by communities, and potential misinterpretation or misappropriation of analyses that could further exacerbate health inequities. Big Data Science can support the HIV response by helping to identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. However, in the absence of a guiding framework for equity, alongside meaningful collaboration with communities through balanced partnerships, a reliance on big data could continue to reinforce inequities within and across marginalized populations.

A method of Mapping Process for scientific production using the Smart Bibliometrics

Big data launches a modern way of producing science and research around the world. Due to an explosion of data available in scientific databases, combined with recent advances in information technology, the researcher has at his disposal new methods and technologies that facilitate scientific development. Considering the challenges of producing science in a dynamic and complex scenario, the main objective of this article is to present a method aligned with tools recently developed to support scientific production, based on steps and technologies that will help researchers to materialize their objectives efficiently and effectively. Applying this method, the researcher can apply science mapping and bibliometric techniques with agility, taking advantage of an easy-to-use solution with cloud computing capabilities. From the application of the "Scientific Mapping Process", the researcher will be able to generate strategic information for a result-oriented scientific production, assertively going through the main steps of research and boosting scientific discovery in the most diverse fields of investigation. •The Scientific Mapping Process provides a method and a system to boost scientific development.•It automates Science Mapping and bibliometric analysis from scientific datasets.•It facilitates the researcher's work, increasing the assertiveness in scientific production.

Global trends in depression among patients living with HIV: A bibliometric analysis

Background

Human immunodeficiency virus (HIV) related depression has seriously affected the quality of life and treatment outcomes of patients living with HIV (PLWH), which has become a hot topic in recent years. This study aims to discover the main keywords, predict frontier topics, and give meaningful suggestions for researchers by bibliometric analysis.

Methods

Publications between 1999 and 2022 on depression in HIV/AIDS were searched in the Web of Science core collection. Microsoft Excel 2010 and VOSviewer were utilized to key contributors (e.g., authors, journals, institutions, and countries). VOSviewer and CiteSpace were used to analyze the knowledge evolution, collaborative maps, hot topics, and keywords trends in this field.

Results

In total, 8,190 publications were included in the final analysis. From 1999 to 2021, the number of published articles roughly presents a steadily increasing trend. The United States, South Africa, and the United Kingdom were three key contributing countries/regions to this field. University Calif San Francisco (United States), University Calif Los Angeles (United States), and Johns Hopkins University (United States) were three key contributing institutions. Safren, Steven A. was the most productive and highest cited author. AIDS Care was the top prolific journal. Antiretroviral therapy and adherence, men has sex with men, mental health, substance abuse, stigma, and Sub-Saharan Africa were the central topics regarding the depression-related research in HIV/AIDS.

Conclusion

This bibliometric analysis reported the publication trend, major contributing countries/regions, institutions, authors, journals and mapped the knowledge network of depression-related research on HIV/AIDS. In this field, topics such as “adherence,” “mental health,” “substance abuse,” “stigma,” “men who have sex with men” and “South Africa” have attracted considerable attention.

Visualizing the knowledge domains and research trends of childhood asthma: A scientometric analysis with CiteSpace

Background

Asthma is one of the most common chronic diseases in children globally. In recent decades, advances have been made in understanding the mechanism, diagnosis, treatment and management for childhood asthma, but few studies have explored its knowledge structure and future interests comprehensively.

Objective

This scientometric study aims to understand the research status and emerging trends of childhood asthma.

Methods

CiteSpace (version 5.8.R3) was used to demonstrate national and institutional collaborations in childhood asthma, analyze research subjects and journal distribution, review research keywords and their clusters, as well as detect research bursts.

Results

A total of 14,340 publications related to childhood asthma were extracted from Web of Science (core database) during January 2011 to December 2021. The results showed that academic activities of childhood asthma had increased steadily in the last decade. Most of the research was conducted by developed countries while China, as a developing country, was also actively engaged in this field. In addition to subjects of allergy and immunology, both public health aspects and ecological environmental impacts on the disease were emphasized recently in this research field. Keywords clustering analysis indicated that research on asthma management and atopy was constantly updated and became the two major research focuses recently, as a significant shift in research hotspots from etiology and diagnosis to atopic march and asthma management was identified. Subgroup analysis for childhood asthma management and atopy suggested that caregiver- or physician-based education and interventions were emerging directions for asthma management, and that asthma should be carefully studied in the context of atopy, together with other allergic diseases.

Conclusions

This study presented a comprehensive and systematic overview of the research status of childhood asthma, provided clues to future research directions, and highlighted two significant research trends of asthma management and atopy in this field.