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Explainable and Interpretable Machine Learning for Antimicrobial Stewardship: Opportunities and Challenges.

Daniele Roberto Giacobbe1, Cristina Marelli2, Sabrina Guastavino3

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Artificial intelligence and machine learning (AI and ML) can enhance antimicrobial stewardship by predicting resistance and recommending treatments. Understanding AI/ML interpretability is key to avoiding bias and ensuring appropriate antimicrobial use.

Keywords:
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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Infectious Disease

Background:

  • Growing interest in AI and ML for antimicrobial stewardship.
  • Need for interpretability and explainability in complex ML models to avoid bias.
  • ML algorithms can predict antimicrobial resistance and recommend therapies.

Purpose of the Study:

  • Review and discuss ML algorithms for antimicrobial stewardship interventions.
  • Highlight opportunities and challenges of ML in this field.
  • Emphasize interpretability and explainability of ML models.

Main Methods:

  • Review of current literature and concepts.
  • Discussion of ML applications in antimicrobial stewardship.
  • Focus on interpretability and explainability aspects.

Main Results:

  • AI and ML show potential to improve antimicrobial stewardship efficacy.
  • ML can reduce time-consuming tasks for healthcare personnel.
  • Enhanced understanding of ML models is crucial for progress.

Conclusions:

  • AI and ML offer significant potential for antimicrobial stewardship.
  • Interpretability and explainability are critical for safe and effective ML implementation.
  • Further research into ML model transparency is needed to combat antimicrobial resistance.