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

Faiza Farhat; Md Tanwir Athar; Sultan Ahmad; Dag Øivind Madsen; Shahab Saquib Sohail

doi:10.3389/fmicb.2023.1179312

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

Front Microbiol. 2023 May 26:14:1179312. doi: 10.3389/fmicb.2023.1179312. eCollection 2023.

Authors

Faiza Farhat¹, Md Tanwir Athar², Sultan Ahmad^{3

4}, Dag Øivind Madsen⁵, Shahab Saquib Sohail⁶

Affiliations

¹ Department of Zoology, Aligarh Muslim University, Aligarh, India.
² Department of Pharmacognosy and Pharmaceutical Chemistry, College of Dentistry and Pharmacy, Buraydah Colleges, Buraydah, Al-Qassim, Saudi Arabia.
³ Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.
⁴ Department of Computer Science and Engineering, University Center for Research and Development (UCRD), Chandigarh University, Mohali, Punjab, India.
⁵ School of Business, University of South-Eastern Norway, Hønefoss, Norway.
⁶ Department of Computer Science and Engineering, Jamia Hamdard University, New Delhi, India.

Abstract

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.

Keywords: antibiotic resistance; antimicrobial resistance; bibliometric analysis; deep learning; healthcare; machine learning.