ArticleInfection and drug resistance2024
Extended Spectrum beta-Lactamase Bacteria and Multidrug Resistance in Jordan are Predicted Using a New Machine-Learning system.
Article in Infection and drug resistance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine learning based prediction of antimicrobial resistanceFrontiers in public health · 2026Article
- Machine Learning-Based Detection of Parkinson's Disease From Arabic Speech: A Cross-Linguistic Validation Study.Journal of central nervous system disease · 2026Article
- Prediction Score for Identification of ESBL Producers in Urinary Infections Overestimates Risk in High-ESBL-Prevalence Setting.Antibiotics (Basel, Switzerland) · 2025Article
- Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing.Parasites & vectors · 2025Article
- Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach.Advances and applications in bioinformatics and chemistry : AABC · 2024Article
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Authors and funding
8 authors.
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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: 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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