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Biosensor for Detection of Antibiotic Resistant Staphylococcus Bacteria
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Extended Spectrum beta-Lactamase Bacteria and Multidrug Resistance in Jordan are Predicted Using a New

Enas M Al-Khlifeh1, Ibrahim S Alkhazi2, Majed Abdullah Alrowaily3

  • 1Department of Medical Laboratory Science, Al-Balqa Applied University, Al-salt, 19117, Jordan.

Infection and Drug Resistance
|July 31, 2024
PubMed
Summary

Machine learning models effectively predict extended-spectrum beta-lactamase (ESBL) and multidrug resistance (MDR) in bacteria. Key predictors include patient age and specific antibiotic classes, aiding in optimized antibiotic therapy.

Keywords:
CART and RFE. coliESBLcefuroximemachine learningmultidrug-resistant bacteria

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Area of Science:

  • Microbiology
  • Medical Informatics
  • Computational Biology

Background:

  • Rising incidence of extended-spectrum beta-lactamase (ESBL) producing microorganisms presents a significant public health challenge.
  • Machine learning (ML) is increasingly utilized to predict bacterial antibiotic resistance for optimizing treatment strategies.
  • This study focuses on applying ML to forecast ESBL and multidrug resistance (MDR) in bacteria.

Purpose of the Study:

  • To employ ML algorithms for predicting the occurrence of ESBL and MDR bacteria.
  • To identify key features associated with ESBL emergence and resistance.
  • To select the optimal ML method for predicting ESBL profiles.

Main Methods:

  • Trained six ML algorithms on 489 antibiotic resistance test patient reports.
  • Utilized microbiological and clinical data to predict ESBL and MDR profiles.
  • Selected optimal ML methods based on predictive performance for identifying associated features.

Main Results:

  • Escherichia coli (E. coli) was the most common ESBL-producing microbe (82%), often associated with urinary tract infections (UTIs, 68.7%).
  • Classification and Regression Trees (CART) and Random Forest (RF) were the most effective ML algorithms.
  • Patient age and antibiotic classes like cefuroxime, ceftazidime, and ciprofloxacin were associated with ESBL, while amikacin and meropenem showed an inverse relationship.

Conclusions:

  • CART and RF ML algorithms can accurately predict key features of ESBL.
  • Monitoring ESBL infection trends is crucial for effective antibiotic therapy administration.
  • ML provides a valuable tool for understanding and combating antimicrobial resistance.