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A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria.

Xinxing Li1, Ziyi Zhang1, Buwen Liang1

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Summary

This review explores antimicrobial resistance (AMR) data analysis and prediction using machine learning. It aims to provide a comprehensive reference for understanding and combating the global challenge of AMR.

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Area of Science:

  • Microbiology
  • Computational Biology
  • Public Health

Background:

  • Antimicrobials are vital for treating infections but their efficacy is threatened by widespread antibiotic resistance.
  • Antibiotic-resistant pathogens pose a significant global health risk to both humans and animals.
  • Analyzing complex, nonlinear antimicrobial resistance (AMR) data is challenging due to large volumes and redundancy.

Purpose of the Study:

  • To review techniques for storing and analyzing AMR data.
  • To examine methods for assessing AMR and associated risks.
  • To explore prediction methods for antimicrobial resistance.

Main Methods:

  • Machine learning approaches.
  • Data mining techniques.
  • Literature review of AMR data storage, analysis, assessment, and prediction.

Main Results:

  • The review covers data management strategies for AMR.
  • It details various methods for AMR risk assessment.
  • It summarizes current research and trends in AMR prediction.

Conclusions:

  • Machine learning and data mining offer powerful tools for tackling AMR challenges.
  • A systematic approach to AMR data analysis and prediction is crucial.
  • This review serves as a reference for future AMR research and development.