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Antimicrobial resistance and machine learning: past, present, and future

Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators su...

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Detalles Bibliográficos
Autores principales: Farhat, Faiza, Athar, Md Tanwir, Ahmad, Sultan, Madsen, Dag Øivind, Sohail, Shahab Saquib
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250749/
https://www.ncbi.nlm.nih.gov/pubmed/37303800
http://dx.doi.org/10.3389/fmicb.2023.1179312
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author Farhat, Faiza
Athar, Md Tanwir
Ahmad, Sultan
Madsen, Dag Øivind
Sohail, Shahab Saquib
author_facet Farhat, Faiza
Athar, Md Tanwir
Ahmad, Sultan
Madsen, Dag Øivind
Sohail, Shahab Saquib
author_sort Farhat, Faiza
collection PubMed
description Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators such as article count, citation count, and the Hirsch index (H-index) to evaluate the relevance and impact of the leading countries, organizations, journals, and authors in this field. VOSviewer and Biblioshiny programs are utilized to analyze citation and co-citation networks, collaboration networks, keyword co-occurrence, and trend analysis. The United States has the highest contribution with 254 articles, accounting for over 37.57% of the total corpus, followed by China (103) and the United Kingdom (78). Among 58 publishers, the top four publishers account for 45% of the publications, with Elsevier leading with 15% of the publications, followed by Springer Nature (12%), MDPI, and Frontiers Media SA with 9% each. Frontiers in Microbiology is the most frequent publication source (33 articles), followed by Scientific Reports (29 articles), PLoS One (17 articles), and Antibiotics (16 articles). The study reveals a substantial increase in research and publications on the use of machine learning to predict antibiotic resistance. Recent research has focused on developing advanced machine learning algorithms that can accurately forecast antibiotic resistance, and a range of algorithms are now being used to address this issue.
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spelling pubmed-102507492023-06-10 Antimicrobial resistance and machine learning: past, present, and future Farhat, Faiza Athar, Md Tanwir Ahmad, Sultan Madsen, Dag Øivind Sohail, Shahab Saquib Front Microbiol Microbiology Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators such as article count, citation count, and the Hirsch index (H-index) to evaluate the relevance and impact of the leading countries, organizations, journals, and authors in this field. VOSviewer and Biblioshiny programs are utilized to analyze citation and co-citation networks, collaboration networks, keyword co-occurrence, and trend analysis. The United States has the highest contribution with 254 articles, accounting for over 37.57% of the total corpus, followed by China (103) and the United Kingdom (78). Among 58 publishers, the top four publishers account for 45% of the publications, with Elsevier leading with 15% of the publications, followed by Springer Nature (12%), MDPI, and Frontiers Media SA with 9% each. Frontiers in Microbiology is the most frequent publication source (33 articles), followed by Scientific Reports (29 articles), PLoS One (17 articles), and Antibiotics (16 articles). The study reveals a substantial increase in research and publications on the use of machine learning to predict antibiotic resistance. Recent research has focused on developing advanced machine learning algorithms that can accurately forecast antibiotic resistance, and a range of algorithms are now being used to address this issue. Frontiers Media S.A. 2023-05-26 /pmc/articles/PMC10250749/ /pubmed/37303800 http://dx.doi.org/10.3389/fmicb.2023.1179312 Text en Copyright © 2023 Farhat, Athar, Ahmad, Madsen and Sohail. 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 Microbiology
Farhat, Faiza
Athar, Md Tanwir
Ahmad, Sultan
Madsen, Dag Øivind
Sohail, Shahab Saquib
Antimicrobial resistance and machine learning: past, present, and future
title Antimicrobial resistance and machine learning: past, present, and future
title_full Antimicrobial resistance and machine learning: past, present, and future
title_fullStr Antimicrobial resistance and machine learning: past, present, and future
title_full_unstemmed Antimicrobial resistance and machine learning: past, present, and future
title_short Antimicrobial resistance and machine learning: past, present, and future
title_sort antimicrobial resistance and machine learning: past, present, and future
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250749/
https://www.ncbi.nlm.nih.gov/pubmed/37303800
http://dx.doi.org/10.3389/fmicb.2023.1179312
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