WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6

Browse

Filters

2022 - 20263

Settings

Subject: search.filters.subject.Antibiotic Resistance

Search Results

Now showing 1 - 3 of 3
  • Article
    Machine Learning Model for Predicting Multidrug Resistance in Clinical Klebsiella pneumoniae Isolates
    (MDPI, 2026) Akkaya, Yuksel; Aydin, Irfan; Tanyildizi-Kokkulunk, Handan; Erturk, Ayse; Kilic, Ibrahim Halil
    Background/Objectives: Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study evaluates machine learning (ML) algorithms for faster antimicrobial resistance prediction than conventional methods. Methods: In this retrospective study, antibiogram results of 607 Klebsiella pneumoniae isolates collected between 2017 and 2024 were combined with demographic and clinical information of the patients from whom the isolates were obtained. Four different ML algorithms, namely Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN) and Random Forest (RF), were applied to classify the resistance status for 22 antibiotics. Model performances were evaluated using accuracy, precision, recall, F-score, AUC and feature importance metrics. Results: The RF model showed the highest overall performance in accurately predicting resistance to 22 antibiotics, achieving an average AUC value of 0.96. In particular, it predicted resistance to treatment-critical antibiotics such as Ertapenem (100%), Imipenem (93%) and Meropenem (95%) with high accuracy. Conclusions: ML models, especially RF, offer a powerful tool for rapid antibiotic resistance prediction, supporting accurate empirical treatment decisions and antimicrobial stewardship.
  • Article
    Machine Learning Model for Predicting Multidrug Resistance in Clinical Escherichia Coli Isolates: A Retrospective General Surgery Study
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Tolan, H.K.; Aydın, İ.; Tanyildizi-Kökkülünk, H.; Karakuş, M.; Akkaya, Y.; Kaya, O.; Işman, F.K.
    Background/Objectives: Escherichia coli is one of the leading causes of surgical site infections (SSIs) and poses a growing public health concern due to its increasing antimicrobial resistance. High rates of extended-spectrum beta-lactamase (ESBL) production among E. coli strains complicate treatment outcomes and emphasize the need for effective surveillance and control strategies. Methods: A total of 691 E. coli isolates from general surgery clinics (2020–2025) were identified using MALDI-TOF MS. Antibiotic susceptibility data and patient variables were cleaned, encoded, and used to predict resistance using the Random Forest, CatBoost, and Naive Bayes algorithms. SMOTE addressed class imbalance, and model performance was assessed through various validation methods. Results: Among the three machine learning models tested, Random Forest (RF) showed the best performance in predicting antibiotic resistance of E. coli, achieving median accuracy, precision, recall, and F1-scores of 0.90 and AUC values up to 0.99 for key antibiotics. CatBoost performed similarly but was less stable with imbalanced data, while Naive Bayes showed lower accuracy. Feature importance analysis highlighted strong inter-antibiotic resistance links, especially among β-lactams, and some influence of demographic factors. Conclusions: This study highlights the potential of simple, high-performing models using structured clinical data to predict antimicrobial resistance, especially in resource-limited clinical settings. By incorporating machine learning into antimicrobial resistance (AMR) surveillance systems, our goal is to support the advancement of rapid diagnostics and targeted antimicrobial stewardship approaches, which are essential in addressing the growing challenge of multidrug resistance. © 2025 by the authors.
  • Review
    Molecular Characterization of Resistance and Virulence Genes in Enterococcus Faecium Strains Isolated Between 2000-2021; Systematic Review
    (Bilimsel Tip Yayinevi, 2022) Kahraman Kilbas, Elmas Pinar; Kilbas, Imdat; Ciftci, Ihsan Hakki; Kılbaş, Elmas Pınar Kahraman
    Introduction: The spread of Vancomycin-Resistant Enterococci (VRE) is a major threat in healthcare institutions, especially for patients in the risk group. The aim of this study is to reveal the antibiotic resistance genes, virulence genes and other accompanying factors detected in vancomycin resistant Enterococcus faecium strains isolated from various clinical specimens in different parts of Turkiye. Material and Methods: For this purpose, a systematic search was carried out using different electronic databases between January 2000 and September 2021. A total of 17 studies were evaluated within the scope of systematic review. Results: The vanA gene was detected the most between the years 2000-2007, and no statistically significant difference was found according to the years. The prevalence of the vanB gene was highest between 2008 and 2013, and no statistical difference was found according to the years (p> 0.05). The vanA gene was mostly detected in Eastern Anatolia, Black Sea, Mediterranean and Aegean, vanB Central Anatolia and Southeastern Anatolia regions. No reports related to the vanC gene were found. Since all strains were E. faecium in our study, it is an expected finding that the vanC gene region was never reported. The esp and hyl gene between 2014-2021. Conclusion: The prevalence of resistance and virulence genes among bacteria is a matter of great concern, limiting treatment options. In particular, effective measures should be taken to prevent healthcare-associated VRE infections, and each institution should report its own resistance data.