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Development of Antibiotic Resistance01:30

Development of Antibiotic Resistance

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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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Machine Learning Model for Predicting Multidrug Resistance in Clinical Escherichia coli Isolates: A Retrospective

Hüseyin Kerem Tolan1, İrfan Aydın2, Handan Tanyildizi-Kokkulunk3

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Machine learning models can predict Escherichia coli antibiotic resistance, aiding surveillance. Random Forest models show high accuracy, supporting antimicrobial stewardship and diagnostics in healthcare settings.

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Area of Science:

  • Clinical microbiology
  • Computational biology
  • Infectious disease epidemiology

Background:

  • Escherichia coli is a primary cause of surgical site infections (SSIs), with rising antimicrobial resistance posing a significant public health threat.
  • High rates of extended-spectrum beta-lactamase (ESBL) production in E. coli complicate treatment and necessitate improved surveillance and control measures.

Purpose of the Study:

  • To evaluate machine learning algorithms for predicting antimicrobial resistance in E. coli isolates from surgical site infections.
  • To identify key predictors of antibiotic resistance, including inter-antibiotic correlations and demographic factors.

Main Methods:

  • Analysis of 691 E. coli isolates from general surgery clinics (2020-2025) identified via MALDI-TOF MS.
  • Application of Random Forest, CatBoost, and Naive Bayes algorithms to predict antibiotic resistance using clinical and susceptibility data.
  • Utilized SMOTE for class imbalance and assessed model performance through various validation metrics.

Main Results:

  • Random Forest demonstrated superior predictive performance, achieving median accuracy, precision, recall, and F1-scores of 0.90 and AUC values up to 0.99 for key antibiotics.
  • CatBoost showed comparable results but was less stable with imbalanced datasets; Naive Bayes had lower accuracy.
  • Feature importance analysis revealed strong links between resistance to different antibiotics, particularly beta-lactams, and some influence from patient demographics.

Conclusions:

  • Simple, high-performing machine learning models can effectively predict antimicrobial resistance using structured clinical data, even in resource-limited settings.
  • Integrating machine learning into antimicrobial resistance (AMR) surveillance can enhance rapid diagnostics and targeted antimicrobial stewardship.
  • These approaches are crucial for combating the increasing challenge of multidrug-resistant E. coli infections.