Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings.

Journal: PloS one

PMID: 39999193

Abstract

BACKGROUND: Antimicrobial resistance (AMR) poses a worldwide health threat; quick and accurate identification of AMR enhances patient outcomes and reduces inappropriate antibiotic usage. The objective of this systematic review is to evaluate the efficacy of machine learning (ML) approaches in predicting AMR in critical and high-priority pathogens (CHPP), considering antimicrobial susceptibility tests in real-world healthcare settings.

Authors

Carlos M Ardila

Basic Sciences Department, Faculty of Dentistry, Universidad de Antioquia, Medellin, Colombia.
Daniel González-Arroyave

Surgery, Universidad Pontificia Bolivariana, Medellin, Colombia.
Sergio Tobón

Postdoctoral Program, CIFE University Center, Cuernavaca, México.

Keywords

Anti-Bacterial Agents Delivery of Health Care Drug Resistance, Bacterial Humans Machine Learning Microbial Sensitivity Tests

External Resources

View on PubMed Access via DOI PubMed (39999193)

Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings.

Abstract

Authors

Keywords

External Resources

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