Volume 20, Issue 1 (Vol.20, No.1, Spring 2024)
irje 2024, 20(1): 65-68 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rahimkhani M, Gilani M. Utilizing Machine Learning to Predict Antimicrobial Resistance in Bacteria. irje 2024; 20 (1) :65-68
URL: http://irje.tums.ac.ir/article-1-7331-en.html
URL: http://irje.tums.ac.ir/article-1-7331-en.html
1- Associated Professor, Dep of Lab Medical Sciences, Faculty of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran , rrahimkhani@sina.tums.ac.ir
2- B.Sc. in Lab Medical Sciences, Faculty of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
2- B.Sc. in Lab Medical Sciences, Faculty of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
Abstract: (356 Views)
Antibiotic resistance has increased significantly in recent years. On the other hand, machine learning (ML) algorithms are increasingly used in medical research and healthcare and are gradually improving clinical performance.
Using ML to fight antimicrobial resistance (AMR) is one of the most critical areas of interest among the various applications of these new methods. The rise of antibiotic resistance and managing multidrug-resistant infections that are difficult to treat are important challenges.
Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance and thus support clinicians in selecting the appropriate treatment. Machine learning and artificial intelligence (AI) in predicting antimicrobial resistance are among today's sciences. Therefore, an antimicrobial stewardship program (ASP) should be implemented to optimize antibiotic prescribing and limit AMR.
Using ML to fight antimicrobial resistance (AMR) is one of the most critical areas of interest among the various applications of these new methods. The rise of antibiotic resistance and managing multidrug-resistant infections that are difficult to treat are important challenges.
Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance and thus support clinicians in selecting the appropriate treatment. Machine learning and artificial intelligence (AI) in predicting antimicrobial resistance are among today's sciences. Therefore, an antimicrobial stewardship program (ASP) should be implemented to optimize antibiotic prescribing and limit AMR.
Type of Study: Letter to Editor |
Subject:
Special
Received: 2024/02/4 | Accepted: 2024/05/27 | Published: 2024/06/12
Received: 2024/02/4 | Accepted: 2024/05/27 | Published: 2024/06/12
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
