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Analyzing systemic lupus erythematosus publications using neural network–based multi-label classification algorithms

The heterogeneity in systemic lupus erythematosus research topics poses a challenge for the entire lupus community, from basic geneticists to clinical investigators. As such, it is critical for medical professionals to remain up to date on directions in lupus research and the main fields in which th...

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Detalles Bibliográficos
Autores principales: Rajabi, Enayat, Sahebari, Maryam, Thomas, Tressy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9112627/
https://www.ncbi.nlm.nih.gov/pubmed/35414318
http://dx.doi.org/10.1177/09612033221093548
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author Rajabi, Enayat
Sahebari, Maryam
Thomas, Tressy
author_facet Rajabi, Enayat
Sahebari, Maryam
Thomas, Tressy
author_sort Rajabi, Enayat
collection PubMed
description The heterogeneity in systemic lupus erythematosus research topics poses a challenge for the entire lupus community, from basic geneticists to clinical investigators. As such, it is critical for medical professionals to remain up to date on directions in lupus research and the main fields in which this research is being conducted (e.g., etiology, diagnosis, treatment, and outcomes). This article develops two multi-label text-classification models using Deep Neural Networks and Convolutional Neural Networks to classify the human-based adult-onset lupus–related articles in the PubMed database based on their abstract, keywords, and MeSH terms. During training evaluation, our models correctly indicated all relevant labels for 70% of the articles. The applied machine learning models (Deep Neural Network and Convolutional Neural Network) yielded a Micro-F1 score of 0.89, meaning that it successfully labeled the most relevant medical domains and types. In addition, these types of studies help the researchers be aware of the essential topics in this field, but due to difficulties in designing, the related studies are ignored or fade.
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spelling pubmed-91126272022-05-18 Analyzing systemic lupus erythematosus publications using neural network–based multi-label classification algorithms Rajabi, Enayat Sahebari, Maryam Thomas, Tressy Lupus Papers The heterogeneity in systemic lupus erythematosus research topics poses a challenge for the entire lupus community, from basic geneticists to clinical investigators. As such, it is critical for medical professionals to remain up to date on directions in lupus research and the main fields in which this research is being conducted (e.g., etiology, diagnosis, treatment, and outcomes). This article develops two multi-label text-classification models using Deep Neural Networks and Convolutional Neural Networks to classify the human-based adult-onset lupus–related articles in the PubMed database based on their abstract, keywords, and MeSH terms. During training evaluation, our models correctly indicated all relevant labels for 70% of the articles. The applied machine learning models (Deep Neural Network and Convolutional Neural Network) yielded a Micro-F1 score of 0.89, meaning that it successfully labeled the most relevant medical domains and types. In addition, these types of studies help the researchers be aware of the essential topics in this field, but due to difficulties in designing, the related studies are ignored or fade. SAGE Publications 2022-04-13 2022-06 /pmc/articles/PMC9112627/ /pubmed/35414318 http://dx.doi.org/10.1177/09612033221093548 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Papers
Rajabi, Enayat
Sahebari, Maryam
Thomas, Tressy
Analyzing systemic lupus erythematosus publications using neural network–based multi-label classification algorithms
title Analyzing systemic lupus erythematosus publications using neural network–based multi-label classification algorithms
title_full Analyzing systemic lupus erythematosus publications using neural network–based multi-label classification algorithms
title_fullStr Analyzing systemic lupus erythematosus publications using neural network–based multi-label classification algorithms
title_full_unstemmed Analyzing systemic lupus erythematosus publications using neural network–based multi-label classification algorithms
title_short Analyzing systemic lupus erythematosus publications using neural network–based multi-label classification algorithms
title_sort analyzing systemic lupus erythematosus publications using neural network–based multi-label classification algorithms
topic Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9112627/
https://www.ncbi.nlm.nih.gov/pubmed/35414318
http://dx.doi.org/10.1177/09612033221093548
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