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|Title:||Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.|
|Authors:||Macesic, Nenad;Polubriaginof, Fernanda;Tatonetti, Nicholas P|
|Affiliation:||Division of Infectious Diseases, Columbia University Medical Center|
Department of Biomedical Informatics, Columbia University, New York City, New York, USA
Department of Infectious Diseases, Austin Health, Heidelberg, Victoria, Australia
|Citation:||Current opinion in infectious diseases 2017; 30(6): 511-517|
|Abstract:||Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality.Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.|
|Appears in Collections:||Journal articles|
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