WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 4Relationship Between Neutrophil Gelatinase-Associated Lipocalin and Mortality in Acute Kidney Injury(Galenos Yayincilik, 2018-12-03) Kayaalti, Selda; Kayaalti, Omer; Aksebzeci, Bekir HakanObjective: Almost half of intensive care patients are affected by acute kidney injury (AKI). The purpose of this study is to determine parameters that can be used for predicting of early (within 28 days) and late (within 90 days) mortality in patients who are followed-up with AKI in intensive care units. Materials and Methods: In this study, a dataset that contains 50 patients with AKI in intensive care units was used. This dataset contains blood urea nitrogen, creatinine, plasma and urinary neutrophil gelatinase-associated hpocalin (NGAL), lactate dehydrogenase, alkaline phosphatase and gammaglutamyl transpeptidase values of patients who were admitted to intensive care for various reasons and who developed AKI on the days 1, 3 and 7. In addition to these values, laboratory results such as serum electrolytes on day 1, blood gas; vital signs such as mean arterial pressure, central venous pressure; and demographic data were also recorded. Data mining techniques were applied to determine correlation between all of these data and mortality. Results: The threshold level of urinary NGAL on day 7 was determined to be 69 ng/mL, and strong correlation was found between this threshold level and early mortality. Similarly, the threshold level of plasma NGAL on day 7 was determined to be 150 ng/mL, and this was highly correlated with early mortality. Besides, strong correlation was also found between the difference in the urinary NGAL levels on day 1 and 7, and early mortality. Conclusion: In this study, plasma and urinary NGAL levels were found to be closely related to early mortality in patients who were followed-up with AKI in intensive care units. On the other hand, any parameter associated with late mortality was not found.Article Citation - WoS: 29Citation - Scopus: 32Liver Fibrosis Staging Using CT Image Texture Analysis and Soft Computing(Elsevier, 2014-12) Kayaalti, Omer; Aksebzeci, Bekir Hakan; Karahan, Ibrahim Okkes; Deniz, Kemal; Ozturk, Mehmet; Yilmaz, Bulent; Asyali, Musa HakanLiver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws' method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws' texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. (C) 2014 Elsevier B.V. All rights reserved.Article A Decision Support System for the Prediction of Mortality in Patients With Acute Kidney Injury Admitted in Intensive Care Unit(Univ South Bohemia, 2020-03-01) Kayaalti, Selda; Kayaalti, Omer; Aksebzeci, Bekir HakanIntensive care unit (ICU) is a very special unit of a hospital, where healthcare professionals provide treatment and, later, close followup to the patients. It is crucial to estimate mortality in ICU patients from many viewpoints. The purpose of this study is to classify the status of patients with acute kidney injury (AKI) in ICU as early mortality, late mortality, and survival by the application of Classification and Regression Trees (CART) algorithm to the patients' attributes such as blood urea nitrogen, creatinine, serum and urine neutrophil gelatinase-associated lipocalin (NGAL), alkaline phosphatase, lactate dehydrogenase (LDH), gamma-glutamyl transferase, laboratory electrolytes, blood gas, mean arterial pressure, central venous pressure and demographic details of patients. This study was conducted 50 patients with AKI who were followed up in the ICU. The study also aims to determine the significance of relationship between the attributes used in the prediction of mortality in CART and patients' status by employing the Kruskal-Wallis H test. The classification accuracy, sensitivity, and specificity of CART for the tested attributes for the prediction of early mortality, late mortality, and survival of patients were 90.00%, 83.33%, and 91.67%, respectively. The values of both urine NGAL and LDH on day 7 showed a considerable difference according to the patients' status after being examined by the Kruskal-Wallis H test.