Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1784709449790783488 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9112627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT rajabienayat analyzingsystemiclupuserythematosuspublicationsusingneuralnetworkbasedmultilabelclassificationalgorithms AT sahebarimaryam analyzingsystemiclupuserythematosuspublicationsusingneuralnetworkbasedmultilabelclassificationalgorithms AT thomastressy analyzingsystemiclupuserythematosuspublicationsusingneuralnetworkbasedmultilabelclassificationalgorithms |