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Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing

OBJECTIVE: This study aims to construct and use natural language processing and other methods to analyze major depressive disorder (MDD) and radiology studies’ publications in the PubMed database to understand the historical growth, current state, and potential expansion trend. METHODS: All MDD radi...

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Autores principales: Wang, Kangtao, Tan, Fengbo, Zhu, Zhiming, Kong, Lingyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748702/
https://www.ncbi.nlm.nih.gov/pubmed/36532194
http://dx.doi.org/10.3389/fpsyt.2022.978763
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author Wang, Kangtao
Tan, Fengbo
Zhu, Zhiming
Kong, Lingyu
author_facet Wang, Kangtao
Tan, Fengbo
Zhu, Zhiming
Kong, Lingyu
author_sort Wang, Kangtao
collection PubMed
description OBJECTIVE: This study aims to construct and use natural language processing and other methods to analyze major depressive disorder (MDD) and radiology studies’ publications in the PubMed database to understand the historical growth, current state, and potential expansion trend. METHODS: All MDD radiology studies publications from January 2002 to January 2022 were downloaded from PubMed using R, a statistical computing language. R and the interpretive general-purpose programming language Python were used to extract publication dates, geographic information, and abstracts from each publication’s metadata for bibliometric analysis. The generative statistical algorithm “Latent Dirichlet allocation” (LDA) was applied to identify specific research focus and trends. The unsupervised Leuven algorithm was used to build a network to identify relationships between research focus. RESULTS: A total of 5,566 publications on MDD and radiology research were identified, and there is a rapid upward trend. The top-cited publications were 11,042, and the highly-cited publications focused on improving diagnostic performance and establishing imaging standards. Publications came from 76 countries, with the most from research institutions in the United States and China. Hospitals and radiology departments take the lead in research and have an advantage. The extensive field of study contains 12,058 Medical Subject Heading (MeSH) terms. Based on the LDA algorithm, three areas were identified that have become the focus of research in recent years, “Symptoms and treatment,” “Brain structure and imaging,” and “Comorbidities research.” CONCLUSION: Latent Dirichlet allocation analysis methods can be well used to analyze many texts and discover recent research trends and focus. In the past 20 years, the research on MDD and radiology has focused on exploring MDD mechanisms, establishing standards, and constructing imaging methods. Recent research focuses are “Symptoms and sleep,” “Brain structure study,” and “functional connectivity.” New progress may be made in studies on MDD complications and the combination of brain structure and metabolism.
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spelling pubmed-97487022022-12-15 Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing Wang, Kangtao Tan, Fengbo Zhu, Zhiming Kong, Lingyu Front Psychiatry Psychiatry OBJECTIVE: This study aims to construct and use natural language processing and other methods to analyze major depressive disorder (MDD) and radiology studies’ publications in the PubMed database to understand the historical growth, current state, and potential expansion trend. METHODS: All MDD radiology studies publications from January 2002 to January 2022 were downloaded from PubMed using R, a statistical computing language. R and the interpretive general-purpose programming language Python were used to extract publication dates, geographic information, and abstracts from each publication’s metadata for bibliometric analysis. The generative statistical algorithm “Latent Dirichlet allocation” (LDA) was applied to identify specific research focus and trends. The unsupervised Leuven algorithm was used to build a network to identify relationships between research focus. RESULTS: A total of 5,566 publications on MDD and radiology research were identified, and there is a rapid upward trend. The top-cited publications were 11,042, and the highly-cited publications focused on improving diagnostic performance and establishing imaging standards. Publications came from 76 countries, with the most from research institutions in the United States and China. Hospitals and radiology departments take the lead in research and have an advantage. The extensive field of study contains 12,058 Medical Subject Heading (MeSH) terms. Based on the LDA algorithm, three areas were identified that have become the focus of research in recent years, “Symptoms and treatment,” “Brain structure and imaging,” and “Comorbidities research.” CONCLUSION: Latent Dirichlet allocation analysis methods can be well used to analyze many texts and discover recent research trends and focus. In the past 20 years, the research on MDD and radiology has focused on exploring MDD mechanisms, establishing standards, and constructing imaging methods. Recent research focuses are “Symptoms and sleep,” “Brain structure study,” and “functional connectivity.” New progress may be made in studies on MDD complications and the combination of brain structure and metabolism. Frontiers Media S.A. 2022-11-30 /pmc/articles/PMC9748702/ /pubmed/36532194 http://dx.doi.org/10.3389/fpsyt.2022.978763 Text en Copyright © 2022 Wang, Tan, Zhu and Kong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Wang, Kangtao
Tan, Fengbo
Zhu, Zhiming
Kong, Lingyu
Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing
title Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing
title_full Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing
title_fullStr Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing
title_full_unstemmed Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing
title_short Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing
title_sort exploring changes in depression and radiology-related publications research focus: a bibliometrics and content analysis based on natural language processing
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748702/
https://www.ncbi.nlm.nih.gov/pubmed/36532194
http://dx.doi.org/10.3389/fpsyt.2022.978763
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