Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests.

Journal: PeerJ

PMID: 39399439

Abstract

BACKGROUND: Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pathogens (CP), considering Whole Genome Sequencing (WGS) and antimicrobial susceptibility testing (AST).

Authors

Carlos M Ardila

Basic Sciences Department, Faculty of Dentistry, Universidad de Antioquia, Medellin, Colombia.
Pradeep K Yadalam

Periodontics, Saveetha University, Saveetha, India.
Daniel González-Arroyave

Surgery, Universidad Pontificia Bolivariana, Medellin, Colombia.

Keywords

Anti-Bacterial Agents Bacteria Drug Resistance, Bacterial Humans Machine Learning Microbial Sensitivity Tests Whole Genome Sequencing

External Resources

View on PubMed Access via DOI PubMed (39399439)

Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests.

Abstract

Authors

Keywords

External Resources

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