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Extended Spectrum beta-Lactamase Bacteria and Multidrug Resistance in Jordan are Predicted Using a New Machine-Learning system.
Al-Khlifeh, Enas M; Alkhazi, Ibrahim S; Alrowaily, Majed Abdullah; Alghamdi, Mansoor; Alrashidi, Malek; Tarawneh, Ahmad S; Alkhawaldeh, Ibraheem M; Hassanat, Ahmad B.
Afiliación
  • Al-Khlifeh EM; Department of Medical Laboratory Science, Al-Balqa Applied University, Al-salt, 19117, Jordan.
  • Alkhazi IS; College of Computers & Information Technology, University of Tabuk, Tabuk, 47512, Saudi Arabia.
  • Alrowaily MA; Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, 72341, Saudi Arabia.
  • Alghamdi M; Computer Science Department, Applied College, University of Tabuk, Tabuk, 71491, Saudi Arabia.
  • Alrashidi M; Computer Science Department, Applied College, University of Tabuk, Tabuk, 71491, Saudi Arabia.
  • Tarawneh AS; Faculty of Information Technology, Mutah University, Al-Karak, Jordan.
  • Alkhawaldeh IM; Faculty of Medicine, Mutah University, Al-Karak, Jordan.
  • Hassanat AB; Faculty of Information Technology, Mutah University, Al-Karak, Jordan.
Infect Drug Resist ; 17: 3225-3240, 2024.
Article en En | MEDLINE | ID: mdl-39081458
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Infect Drug Resist Año: 2024 Tipo del documento: Article País de afiliación: Jordania Pais de publicación: Nueva Zelanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Infect Drug Resist Año: 2024 Tipo del documento: Article País de afiliación: Jordania Pais de publicación: Nueva Zelanda