تجاوز إلى المحتوى الرئيسي

Extended Spectrum beta-Lactamase Bacteria and Multidrug Resistance in Jordan are Predicted Using a New Machine-Learning system

Author name : Majed Abdullah Alrowaily
Publication Date : 2024-07-17
Journal Name : Infection and Drug Resistance

Abstract

Background
The incidence of microorganisms with extended-spectrum beta-lactamase (ESBL) is on the rise, posing a significant public health concern. The current application of machine learning (ML) focuses on predicting bacterial resistance to optimize antibiotic therapy. This study employs ML to forecast the occurrence of bacteria that generate ESBL and demonstrate resistance to multiple antibiotics (MDR).

Methods
Six popular ML algorithms were initially trained on antibiotic resistance test patient reports (n = 489) collected from Al-Hussein/Salt Hospital in Jordan. Trained outcome models predict ESBL and multidrug resistance profiles based on microbiological and patients’ clinical data. The results were utilized to select the optimal ML method to predict ESBL’s most associated features.

Results
Escherichia coli (E. coli, 82%) was the most commonly identified microbe generating ESBL, displaying multidrug resistance. Urinary tract infections (UTIs) constituted the most frequently observed clinical diagnosis (68.7%). Classification and Regression Trees (CART) and Random Forest (RF) classifiers emerged as the most effective algorithms. The relevant features associated with the emergence of ESBL include age and different classes of antibiotics, including cefuroxime, ceftazidime, cefepime, trimethoprim/ sulfamethoxazole, ciprofloxacin, and gentamicin. Fosfomycin nitrofurantoin, piperacillin/tazobactam, along with amikacin, meropenem, and imipenem, had a pronounced inverse relationship with the ESBL class.

Conclusion
CART and RF-based ML algorithms can be employed to predict the most important features of ESBL. The significance of monitoring trends in ESBL infections is emphasized to facilitate the administration of appropriate antibiotic therapy.

Keywords

ESBL, machine learning, multidrug-resistant bacteria, E. coli, cefuroxime, CART and RF

Publication Link

https://www.tandfonline.com/doi/full/10.2147/IDR.S469877

Block_researches_list_suggestions

Suggestions to read

HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things
Ahlem . Harchy Ep Berguiga
Generalized first approximation Matsumoto metric
AMR SOLIMAN MAHMOUD HASSAN
Structure–Performance Relationship of Novel Azo-Salicylaldehyde Disperse Dyes: Dyeing Optimization and Theoretical Insights
EBTSAM KHALEFAH H ALENEZY
“Synthesis and Characterization of SnO₂/α-Fe₂O₃, In₂O₃/α-Fe₂O₃, and ZnO/α-Fe₂O₃ Thin Films: Photocatalytic and Antibacterial Applications”
Asma Arfaoui
تواصل معنا