Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective,Clinical Microbiology Reviews

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Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective
Clinical Microbiology Reviews ( IF 19.0 ) Pub Date : 2022-05-25 , DOI: 10.1128/cmr.00179-21
Jee In Kim _{1,

2,

3} , Finlay Maguire _{1,

2,

4,

5,

6} , Kara K Tsang ₇ , Theodore Gouliouris _{8,

9,

10} , Sharon J Peacock ₈ , Tim A McAllister ₃ , Andrew G McArthur _{11,

12,

13} , Robert G Beiko _{1,

2}

Affiliation

Faculty of Computer Science, Dalhousie Universitygrid.55602.34, Halifax, Canada.
Institute for Comparative Genomics, Dalhousie Universitygrid.55602.34, Halifax, Canada.
Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, Canada.
Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie Universitygrid.55602.34, Halifax, Canada.
Shared Hospital Laboratory, Toronto, Canada.
Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.
London School of Hygiene & Tropical Medicine, London, United Kingdom.
Department of Medicine, University of Cambridgegrid.5335.0, Cambridge, United Kingdom.
Clinical Microbiology and Public Health Laboratory, Public Health England, Cambridge, United Kingdom.
Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
David Braley Centre for Antibiotic Discovery, McMaster Universitygrid.25073.33, Hamilton, Canada.
M.G. DeGroote Institute for Infectious Disease Research, McMaster Universitygrid.25073.33, Hamilton, Canada.
Department of Biochemistry and Biomedical Sciences, McMaster Universitygrid.25073.33, Hamilton, Canada.