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Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA
Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522017/ https://www.ncbi.nlm.nih.gov/pubmed/34713157 http://dx.doi.org/10.3389/fdgth.2021.686720 |
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author | Gupta, Akash Aeron, Shrey Agrawal, Anjali Gupta, Himanshu |
author_facet | Gupta, Akash Aeron, Shrey Agrawal, Anjali Gupta, Himanshu |
author_sort | Gupta, Akash |
collection | PubMed |
description | Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet Allocation (LDA) model with 25 topics using natural language processing (NLP) techniques on PubMed® research articles about “COVID.” We propose a novel methodology to develop and visualise temporal trends, and improve existing online literature hubs. Our results for temporal evolution demonstrate interesting trends, for example, the prominence of “Mental Health” and “Socioeconomic Impact” increased, “Genome Sequence” decreased, and “Epidemiology” remained relatively constant. Applying our methodology to LitCovid, a literature hub from the National Center for Biotechnology Information, we improved the breadth and depth of research topics by subdividing their pre-existing categories. Our topic model demonstrates that research on “masks” and “Personal Protective Equipment (PPE)” is skewed toward clinical applications with a lack of population-based epidemiological research. |
format | Online Article Text |
id | pubmed-8522017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85220172021-10-27 Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA Gupta, Akash Aeron, Shrey Agrawal, Anjali Gupta, Himanshu Front Digit Health Digital Health Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet Allocation (LDA) model with 25 topics using natural language processing (NLP) techniques on PubMed® research articles about “COVID.” We propose a novel methodology to develop and visualise temporal trends, and improve existing online literature hubs. Our results for temporal evolution demonstrate interesting trends, for example, the prominence of “Mental Health” and “Socioeconomic Impact” increased, “Genome Sequence” decreased, and “Epidemiology” remained relatively constant. Applying our methodology to LitCovid, a literature hub from the National Center for Biotechnology Information, we improved the breadth and depth of research topics by subdividing their pre-existing categories. Our topic model demonstrates that research on “masks” and “Personal Protective Equipment (PPE)” is skewed toward clinical applications with a lack of population-based epidemiological research. Frontiers Media S.A. 2021-07-06 /pmc/articles/PMC8522017/ /pubmed/34713157 http://dx.doi.org/10.3389/fdgth.2021.686720 Text en Copyright © 2021 Gupta, Aeron, Agrawal and Gupta. 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 | Digital Health Gupta, Akash Aeron, Shrey Agrawal, Anjali Gupta, Himanshu Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA |
title | Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA |
title_full | Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA |
title_fullStr | Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA |
title_full_unstemmed | Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA |
title_short | Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA |
title_sort | trends in covid-19 publications: streamlining research using nlp and lda |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522017/ https://www.ncbi.nlm.nih.gov/pubmed/34713157 http://dx.doi.org/10.3389/fdgth.2021.686720 |
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