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Antibiotic Selection00:57

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Application of the Intelligent High-Throughput Antimicrobial Sensitivity Testing/Phage Screening System and Lar Index of Antimicrobial Resistance
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Towards personalized guidelines: using machine-learning algorithms to guide antimicrobial selection.

Ed Moran1, Esther Robinson2, Christopher Green3,4

  • 1Southmead Hospital, North Bristol NHS Trust, Bristol BS10 5NB, UK.

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|June 17, 2020
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Summary
This summary is machine-generated.

Machine learning can predict antibiotic resistance in Gram-negative infections. This tool could reduce broad-spectrum antibiotic use by 40%, though validation is needed.

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Area of Science:

  • Infectious Diseases
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Electronic decision support systems aim to minimize inappropriate antibiotic prescribing.
  • Assessing machine learning for predicting antibiotic resistance in Gram-negative bacteria is crucial.

Purpose of the Study:

  • To evaluate an open-source machine learning algorithm for predicting antibiotic resistance.
  • To compare the algorithm's performance against clinical staff in guiding antibiotic selection.

Main Methods:

  • Retrospective analysis of clinical data (2010-2016) from 15,695 UK hospital admissions.
  • Trained XGBoost machine learning algorithm on demographic, microbiology, and prescribing data.
  • Developed point-scoring tools and compared performance using AUC of receiver operating characteristic curves.

Main Results:

  • Point-scoring tools showed limited improvement over medical staff (AUC 0.61-0.67).
  • The machine learning system achieved a marginally better AUC of 0.70.
  • The algorithm could potentially reduce unnecessary broad-spectrum antibiotic use by up to 40%.

Conclusions:

  • Machine learning algorithms show promise in predicting antimicrobial resistance for Gram-negative infections.
  • Further prospective studies are essential to validate performance, assess clinical acceptability, and determine patient outcome impact.