Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/11727/4809
Browse
2 results
Search Results
Item Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests(2021) Cubukcu, Hikmet Can; Topcu, Deniz Ilhan; Bayraktar, Nilufer; Gulsen, Murat; Sari, Nuran; Arslan, Ayse Hande; 0000-0002-1219-6368; 0000-0002-7886-3688; 34791032; E-3717-2019; Y-8758-2018Objectives The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. Methods We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. Results The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). Conclusions ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses.Item Risk Factors for Urinary Tract Infection After Kidney Transplant: A Retrospective Analysis(2020) Tekkarimaz, Nihan; Ozelsancak, Ruya; Micozkadioglu, Hasan; Caliskan, Kenan; Demiroglu, Yusuf Ziya; Arslan, Ayse Hande; H, Mehmet; 0000-0001-5142-5672; 0000-0001-7631-7395; 0000-0002-0788-8319; 0000-0002-8767-5021; 0000-0002-3462-7632; 31424358; AAE-7608-2021; AAD-9088-2021; AAD-5716-2021; AAJ-7201-2021; AAJ-8097-2021Objectives: Urinary tract infections are the most common type of infections in kidney transplant recipients. They are also important factors for increased morbidity and mortality. The aims of this study were to evaluate the number of urinary tract infections, to identify possible donor/receiver-based risk factors, and to evaluate the impact of these infections on graft function. Materials and Methods: Medical records of patients who had undergone kidney transplant between 2010 and 2017 were retrospectively analyzed. Results: Our study included 145 patients (49 women [33.8%] and 96 men [66.2%]), with mean age of 35.2 +/- 12.4 years. There were 105 episodes of urinary tract infections in 55 of 145 patients (37.9%) during the first year after transplant. Female sex (P = .001), glomerulonephritis as primary kidney disease (P = .04), pretransplant diabetes (P = .05), and presence of ureteral stent (P = .03) were significant risk factors for the development of urinary tract infections. The most frequent pathogens identified were Escherichia coli and Klebsiella pneumoniae. Mean glomerular filtration rate at 12 months was significantly lower in patients with urinary tract infection than in patients without infection (80 +/- 25 vs 68 +/- 28 mL/min; P = .006). Conclusions: In kidney transplant recipients, urinary tract infections are common complications and have negative outcomes on graft function. These infections remain an important disease that requires frequent investigations and new ways of approach for prevention.