A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis

Chenyang Yu; Yingzhao Huang; Wei Yan; Xian Jiang

doi:10.3389/fimmu.2023.1272080

A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis

Front Immunol. 2023 Oct 26:14:1272080. doi: 10.3389/fimmu.2023.1272080. eCollection 2023.

Authors

Chenyang Yu^{1

2}, Yingzhao Huang³, Wei Yan¹, Xian Jiang^{1

2}

Affiliations

¹ Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China.
² Laboratory of Dermatology, Clinical Institute of Inflammation and Immunology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
³ Department of Thoracic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

Abstract

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.

Keywords: bibliometric; latent Dirichlet allocation (LDA) algorithm; machine learning; natural language processing (NLP); psoriasis.

Publication types

Systematic Review
Research Support, Non-U.S. Gov't

MeSH terms

Arthritis, Psoriatic*
Bibliometrics
Dermatitis*
Humans
Inflammation
Machine Learning
Psoriasis* / pathology
United States

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the National Natural Science Foundation of China (82273559, 82073473) and the 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (ZYJC21036).