Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review
Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Antibiotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6382/12/3/452 |
| _version_ | 1827751712348176384 |
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| author | Aikaterini Sakagianni Christina Koufopoulou Georgios Feretzakis Dimitris Kalles Vassilios S. Verykios Pavlos Myrianthefs Georgios Fildisis |
| author_facet | Aikaterini Sakagianni Christina Koufopoulou Georgios Feretzakis Dimitris Kalles Vassilios S. Verykios Pavlos Myrianthefs Georgios Fildisis |
| author_sort | Aikaterini Sakagianni |
| collection | DOAJ |
| description | Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician’s point of view. |
| first_indexed | 2024-03-11T07:01:57Z |
| format | Article |
| id | doaj.art-d3816d852194464ebc3ef947b01bf885 |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-03-11T07:01:57Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-d3816d852194464ebc3ef947b01bf8852023-11-17T09:13:10ZengMDPI AGAntibiotics2079-63822023-02-0112345210.3390/antibiotics12030452Using Machine Learning to Predict Antimicrobial Resistance―A Literature ReviewAikaterini Sakagianni0Christina Koufopoulou1Georgios Feretzakis2Dimitris Kalles3Vassilios S. Verykios4Pavlos Myrianthefs5Georgios Fildisis6Intensive Care Unit, Sismanogleio General Hospital, 15126 Marousi, Greece1st Anesthesiology Department, Aretaieio Hospital, National and Kapodistrian University of Athens Medical School, 11528 Athens, GreeceSchool of Science and Technology, Hellenic Open University, 26335 Patras, GreeceSchool of Science and Technology, Hellenic Open University, 26335 Patras, GreeceSchool of Science and Technology, Hellenic Open University, 26335 Patras, GreeceFaculty of Nursing, School of Health Sciences, National and Kapodistrian University of Athens, 11527 Athens, GreeceFaculty of Nursing, School of Health Sciences, National and Kapodistrian University of Athens, 11527 Athens, GreeceMachine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician’s point of view.https://www.mdpi.com/2079-6382/12/3/452machine learningartificial intelligenceantimicrobial resistanceAMRantibiotic stewardshipclinical decision support tools |
| spellingShingle | Aikaterini Sakagianni Christina Koufopoulou Georgios Feretzakis Dimitris Kalles Vassilios S. Verykios Pavlos Myrianthefs Georgios Fildisis Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review Antibiotics machine learning artificial intelligence antimicrobial resistance AMR antibiotic stewardship clinical decision support tools |
| title | Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review |
| title_full | Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review |
| title_fullStr | Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review |
| title_full_unstemmed | Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review |
| title_short | Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review |
| title_sort | using machine learning to predict antimicrobial resistance a literature review |
| topic | machine learning artificial intelligence antimicrobial resistance AMR antibiotic stewardship clinical decision support tools |
| url | https://www.mdpi.com/2079-6382/12/3/452 |
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