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Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review.

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Machine learning (ML) algorithms aid in predicting antibiotic resistance, supporting clinicians in choosing effective treatments. This technology is crucial for antimicrobial resistance (AMR) stewardship and combating multidrug-resistant infections.

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Area of Science:

  • Medical Informatics
  • Infectious Diseases
  • Computational Biology

Background:

  • Antimicrobial resistance (AMR) poses a significant global health threat, necessitating advanced strategies.
  • Decreasing antibiotic efficacy and limited treatment options highlight the urgent need for effective antimicrobial stewardship programs.
  • Machine learning (ML) and artificial intelligence (AI) offer novel approaches to address AMR challenges.

Purpose of the Study:

  • To review the literature on ML and AI applications in antimicrobial resistance prediction.
  • To discuss the value of ML as a complementary tool in antibiotic stewardship from a clinical perspective.

Main Methods:

  • Narrative review of existing literature.
  • Analysis of ML and AI applications in AMR prediction.
  • Focus on clinical utility in antibiotic stewardship.

Main Results:

  • ML algorithms, both supervised and unsupervised, have demonstrated success in predicting early antibiotic resistance.
  • ML tools can assist clinicians in selecting appropriate antimicrobial therapies.
  • AI and ML show promise in enhancing antimicrobial stewardship practices.

Conclusions:

  • ML and AI are valuable complementary tools for combating antimicrobial resistance.
  • Implementing ML in clinical practice can improve the management of multidrug-resistant infections.
  • Further integration of ML into antibiotic stewardship is essential to mitigate the AMR crisis.