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Investigating diseases and chemicals in COVID-19 literature with text mining

Given the rapidly unfolding nature of the COVID-19 pandemic, there is an urgent need to streamline the literature synthesis of the growing scientific research to elucidate targeted solutions. Traditional systematic literature review studies have restrictions, including analyzing a limited number of...

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Detalles Bibliográficos
Autores principales: Karami, Amir, Bookstaver, Brandon, Nolan, Melissa, Bozorgi, Parisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8126089/
http://dx.doi.org/10.1016/j.jjimei.2021.100016
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author Karami, Amir
Bookstaver, Brandon
Nolan, Melissa
Bozorgi, Parisa
author_facet Karami, Amir
Bookstaver, Brandon
Nolan, Melissa
Bozorgi, Parisa
author_sort Karami, Amir
collection PubMed
description Given the rapidly unfolding nature of the COVID-19 pandemic, there is an urgent need to streamline the literature synthesis of the growing scientific research to elucidate targeted solutions. Traditional systematic literature review studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, and lack of data-driven tools. This research has collected 9298 papers representing COVID-19 research published through May 5, 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling that provided 25 categories showing associations between the two overarching categories. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.
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spelling pubmed-81260892021-05-17 Investigating diseases and chemicals in COVID-19 literature with text mining Karami, Amir Bookstaver, Brandon Nolan, Melissa Bozorgi, Parisa International Journal of Information Management Data Insights Article Given the rapidly unfolding nature of the COVID-19 pandemic, there is an urgent need to streamline the literature synthesis of the growing scientific research to elucidate targeted solutions. Traditional systematic literature review studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, and lack of data-driven tools. This research has collected 9298 papers representing COVID-19 research published through May 5, 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling that provided 25 categories showing associations between the two overarching categories. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19. The Author(s). Published by Elsevier Ltd. 2021-11 2021-05-16 /pmc/articles/PMC8126089/ http://dx.doi.org/10.1016/j.jjimei.2021.100016 Text en © 2021 The Author(s). Published by Elsevier Ltd. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Karami, Amir
Bookstaver, Brandon
Nolan, Melissa
Bozorgi, Parisa
Investigating diseases and chemicals in COVID-19 literature with text mining
title Investigating diseases and chemicals in COVID-19 literature with text mining
title_full Investigating diseases and chemicals in COVID-19 literature with text mining
title_fullStr Investigating diseases and chemicals in COVID-19 literature with text mining
title_full_unstemmed Investigating diseases and chemicals in COVID-19 literature with text mining
title_short Investigating diseases and chemicals in COVID-19 literature with text mining
title_sort investigating diseases and chemicals in covid-19 literature with text mining
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8126089/
http://dx.doi.org/10.1016/j.jjimei.2021.100016
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