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Area of Science:

  • Microbiology
  • Computational Biology
  • Genomics

Background:

  • Antimicrobial resistance (AMR) poses a significant global health threat.
  • Novel strategies are crucial for combating the rise of AMR.
  • Machine learning (ML) applications in AMR research are emerging.

Purpose of the Study:

  • To review the current literature on ML applications in studying bacterial AMR.
  • To assess the potential and limitations of ML in addressing AMR.

Main Methods:

  • Literature review of ML applications in bacterial AMR.
  • Analysis of studies utilizing large-scale datasets (e.g., next-generation sequencing, electronic health records).

Main Results:

  • ML has been applied to antimicrobial susceptibility genotype/phenotype prediction.
  • ML aids in developing clinical decision rules and discovering novel antimicrobial agents.
  • ML assists in optimizing antimicrobial therapy.

Conclusions:

  • ML application in AMR study is feasible but currently limited.
  • Clinical implementation faces barriers: model interpretability and data quality.
  • Future ML applications are expected to focus on laboratory-based tasks like phenotype prediction.