WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6
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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.Article Citation - Scopus: 8The Perceptions of Generation Z University Students About Their Futures: A Qualitative Study(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Dikeç, Gül; Öztürk, Simge; Taşbaşı, Neslihan; Figenergül, Damla; Güler, Bilal BuğrahanThis study explored the future-oriented perceptions of Generation Z students in a foundation university. This study was conducted using qualitative research and a phenomenological design. The study sample consisted of 11 university students over the age of 18 who agreed to participate in the study. Data were collected online through individual interviews in Türkiye. Colaizzi’s phenomenological analysis method was used in the data analysis. The content analysis determined three main themes and eleven sub-themes. The first theme was the students’ knowledge acquisition about the “current situation of the country.” Under this theme were four sub-themes: economic problems, the immigrant situation, the education and justice system, and the country’s agenda. In the second theme, students shared their opinions about “being a student in the country.” This theme included economic impossibilities, their participation in limited social activities, and housing problems. In the last theme, “future anxiety,” the sub-themes of the students were found to include experiences hopelessness versus hope. Uncertainty caused anxiety, as did going abroad, finding a job, and improving themselves. It was determined that the participants were worried about the current situation in the countries they lived in during this period due to economic problems; while some were hopeful about the future, some were hopeless and would go abroad. This study might contribute to the literature on determining the future-oriented perceptions, possible stressors and hope levels of Generation Z university students in Türkiye. Additionally, intervention programs can be developed for the management these stressors to protect the mental health of Generation Z university students. On the other hand, it is necessary to protect the mental health of young people, who are the adults of the future, and to create policies for the youth of this country where social opportunities are maintained. © 2024 Elsevier B.V., All rights reserved.