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Curr Opin Infect Dis. 2017 Dec;30(6):511-517. doi: 10.1097/QCO.0000000000000406.

Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

Author information

1
aDivision of Infectious Diseases, Columbia University Medical Center bDepartment of Biomedical Informatics, Columbia University, New York City, New York, USA cDepartment of Infectious Diseases, Austin Health, Heidelberg, Victoria, Australia.

Abstract

PURPOSE OF REVIEW:

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.

RECENT FINDINGS:

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.

SUMMARY:

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.

PMID:
28914640
DOI:
10.1097/QCO.0000000000000406
[Indexed for MEDLINE]

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