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A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis
BACKGROUND: The surge in the number of publications on psoriasis has posed significant challenges for researchers in effectively managing the vast amount of information. However, due to the lack of tools to process metadata, no comprehensive bibliometric analysis has been conducted. OBJECTIVES: This...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637956/ https://www.ncbi.nlm.nih.gov/pubmed/37954610 http://dx.doi.org/10.3389/fimmu.2023.1272080 |
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author | Yu, Chenyang Huang, Yingzhao Yan, Wei Jiang, Xian |
author_facet | Yu, Chenyang Huang, Yingzhao Yan, Wei Jiang, Xian |
author_sort | Yu, Chenyang |
collection | PubMed |
description | BACKGROUND: The surge in the number of publications on psoriasis has posed significant challenges for researchers in effectively managing the vast amount of information. However, due to the lack of tools to process metadata, no comprehensive bibliometric analysis has been conducted. OBJECTIVES: This study is to evaluate the trends and current hotspots of psoriatic research from a macroscopic perspective through a bibliometric analysis assisted by machine learning based semantic analysis. METHODS: Publications indexed under the Medical Subject Headings (MeSH) term “Psoriasis” from 2003 to 2022 were extracted from PubMed. The generative statistical algorithm latent Dirichlet allocation (LDA) was applied to identify specific topics and trends based on abstracts. The unsupervised Louvain algorithm was used to establish a network identifying relationships between topics. RESULTS: A total of 28,178 publications were identified. The publications were derived from 176 countries, with United States, China, and Italy being the top three countries. For the term “psoriasis”, 9,183 MeSH terms appeared 337,545 times. Among them, MeSH term “Severity of illness index”, “Treatment outcome”, “Dermatologic agents” occur most frequently. A total of 21,928 publications were included in LDA algorithm, which identified three main areas and 50 branched topics, with “Molecular pathogenesis”, “Clinical trials”, and “Skin inflammation” being the most increased topics. LDA networks identified “Skin inflammation” was tightly associated with “Molecular pathogenesis” and “Biological agents”. “Nail psoriasis” and “Epidemiological study” have presented as new research hotspots, and attention on topics of comorbidities, including “Cardiovascular comorbidities”, “Psoriatic arthritis”, “Obesity” and “Psychological disorders” have increased gradually. CONCLUSIONS: Research on psoriasis is flourishing, with molecular pathogenesis, skin inflammation, and clinical trials being the current hotspots. The strong association between skin inflammation and biologic agents indicated the effective translation between basic research and clinical application in psoriasis. Besides, nail psoriasis, epidemiological study and comorbidities of psoriasis also draw increased attention. |
format | Online Article Text |
id | pubmed-10637956 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106379562023-11-11 A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis Yu, Chenyang Huang, Yingzhao Yan, Wei Jiang, Xian Front Immunol Immunology BACKGROUND: The surge in the number of publications on psoriasis has posed significant challenges for researchers in effectively managing the vast amount of information. However, due to the lack of tools to process metadata, no comprehensive bibliometric analysis has been conducted. OBJECTIVES: This study is to evaluate the trends and current hotspots of psoriatic research from a macroscopic perspective through a bibliometric analysis assisted by machine learning based semantic analysis. METHODS: Publications indexed under the Medical Subject Headings (MeSH) term “Psoriasis” from 2003 to 2022 were extracted from PubMed. The generative statistical algorithm latent Dirichlet allocation (LDA) was applied to identify specific topics and trends based on abstracts. The unsupervised Louvain algorithm was used to establish a network identifying relationships between topics. RESULTS: A total of 28,178 publications were identified. The publications were derived from 176 countries, with United States, China, and Italy being the top three countries. For the term “psoriasis”, 9,183 MeSH terms appeared 337,545 times. Among them, MeSH term “Severity of illness index”, “Treatment outcome”, “Dermatologic agents” occur most frequently. A total of 21,928 publications were included in LDA algorithm, which identified three main areas and 50 branched topics, with “Molecular pathogenesis”, “Clinical trials”, and “Skin inflammation” being the most increased topics. LDA networks identified “Skin inflammation” was tightly associated with “Molecular pathogenesis” and “Biological agents”. “Nail psoriasis” and “Epidemiological study” have presented as new research hotspots, and attention on topics of comorbidities, including “Cardiovascular comorbidities”, “Psoriatic arthritis”, “Obesity” and “Psychological disorders” have increased gradually. CONCLUSIONS: Research on psoriasis is flourishing, with molecular pathogenesis, skin inflammation, and clinical trials being the current hotspots. The strong association between skin inflammation and biologic agents indicated the effective translation between basic research and clinical application in psoriasis. Besides, nail psoriasis, epidemiological study and comorbidities of psoriasis also draw increased attention. Frontiers Media S.A. 2023-10-26 /pmc/articles/PMC10637956/ /pubmed/37954610 http://dx.doi.org/10.3389/fimmu.2023.1272080 Text en Copyright © 2023 Yu, Huang, Yan and Jiang 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 | Immunology Yu, Chenyang Huang, Yingzhao Yan, Wei Jiang, Xian A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis |
title | A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis |
title_full | A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis |
title_fullStr | A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis |
title_full_unstemmed | A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis |
title_short | A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis |
title_sort | comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637956/ https://www.ncbi.nlm.nih.gov/pubmed/37954610 http://dx.doi.org/10.3389/fimmu.2023.1272080 |
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