year 12, Issue 2 (11-2025)
CJS 2025, 12(2): 96-107 |
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Mohammadi Kouhbanani A, Gholamrezaei R. Scientometric analysis of Natural Language Processing (NLP) technologies applications in public health. CJS 2025; 12 (2) :96-107
URL: http://cjs.mubabol.ac.ir/article-1-413-en.html
URL: http://cjs.mubabol.ac.ir/article-1-413-en.html
Department of Computer Engineering, Ke.C., Islamic Azad University, Kerman, Iran , aminmohamadi.info@gmail.com
Abstract: (305 Views)
Background and aim: Given the challenges facing developing countries in achieving the Sustainable Development Goals in the field of health, the aim of this study was to examine and analyze the growth trends of published articles on the use of NLP technologies in improving public health and to identify key barriers and opportunities in this area.
Materials and methods: The present study was conducted in an applied and descriptive manner, and its statistical population includes articles related to NLP and public health which have been indexed in the Scopus database between 2015 and 2025. To search for relevant articles, a systematic and targeted search strategy using relevant keywords was used. Finally, 1,504 articles were selected as the final statistical population and were examined using VOSviewer software to draw thematic networks and analyze data.
Findings: The results indicate a significant increase in the number of publications in this field, rising from 56 articles in 2015 to 261 articles in 2025. The analysis of co-occurrence network of keywords related to NLP in public health identified six main clusters. These clusters include artificial intelligence, machine learning, natural language processing, health monitoring, and the Internet of Things (IoT), highlighting the complex interactions between emerging technologies and public health challenges, and emphasizing the importance of integrating these technologies to improve health outcomes. Additionally, Pearson correlation test results indicate a positive and significant relationship between the number of published articles and the number of citations at the level of one percent (P<0.001).
Conclusion: In times of global crises, the use of these technologies for collecting and analyzing health data is essential. It is recommended that researchers explore specific applications of NLP in various health domains and strengthen interdisciplinary collaborations to enhance the quality and impact of research.
Materials and methods: The present study was conducted in an applied and descriptive manner, and its statistical population includes articles related to NLP and public health which have been indexed in the Scopus database between 2015 and 2025. To search for relevant articles, a systematic and targeted search strategy using relevant keywords was used. Finally, 1,504 articles were selected as the final statistical population and were examined using VOSviewer software to draw thematic networks and analyze data.
Findings: The results indicate a significant increase in the number of publications in this field, rising from 56 articles in 2015 to 261 articles in 2025. The analysis of co-occurrence network of keywords related to NLP in public health identified six main clusters. These clusters include artificial intelligence, machine learning, natural language processing, health monitoring, and the Internet of Things (IoT), highlighting the complex interactions between emerging technologies and public health challenges, and emphasizing the importance of integrating these technologies to improve health outcomes. Additionally, Pearson correlation test results indicate a positive and significant relationship between the number of published articles and the number of citations at the level of one percent (P<0.001).
Conclusion: In times of global crises, the use of these technologies for collecting and analyzing health data is essential. It is recommended that researchers explore specific applications of NLP in various health domains and strengthen interdisciplinary collaborations to enhance the quality and impact of research.
Type of Study: Orginal |
Subject:
Scientometrics
Received: 2025/10/18 | Revised: 2025/12/19 | Accepted: 2025/12/23 | ePublished: 2025/12/28
Received: 2025/10/18 | Revised: 2025/12/19 | Accepted: 2025/12/23 | ePublished: 2025/12/28
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