Tıp Fakültesi / Faculty of Medicine
Permanent URI for this collectionhttps://hdl.handle.net/11727/1403
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Item Evaluation of Machine Learning Algorithms for Renin-Angiotensin-Aldosterone System Inhibitors Associated Renal Adverse Event Prediction(2023) Guven, Alper Tuna; Ozdede, Murat; Sener, Yusuf Ziya; Yildirim, Ali Osman; Altintop, Sabri Engin; Yesilyurt, Berkay; Uyaroglu, Oguz Abdullah; Tanriover, Mine Durusu; 0000-0002-6310-4240; 37217407Background: Renin-angiotensin-aldosterone system inhibitors (RAASi) are commonly used medications. Renal adverse events associated with RAASi are hyperkalemia and acute kidney injury. We aimed to evaluate the performance of machine learning (ML) algorithms in order to define event associated features and predict RAASi associated renal adverse events.Materials and Methods: Data of patients recruited from five internal medicine and cardiology outpatient clinics were evaluated retrospectively. Clinical, laboratory, and medication data were acquired via electronic medical records. Dataset balancing and feature selection for machine learning algorithms were performed. Random forest (RF), k-nearest neighbor (kNN), naive Bayes (NB), extreme gradient boosting (xGB), support vector machine (SVM), neural network (NN), and logistic regression (LR) were used to create a prediction model.Results: 409 patients were included, and 50 renal adverse events occurred. The most important features predicting the renal adverse events were the index K and glucose levels, as well as having uncontrolled diabetes mellitus. Thiazides reduced RAASi associated hyperkalemia. kNN, RF, xGB and NN algorithms have the highest and similar AUC (> 98%), recall (> 94%), specifity (> 97%), precision (> 92%), accuracy (> 96%) and F1 statistics (> 94%) performance metrics for prediction.Conclusion: RAASi associated renal adverse events can be predicted prior to medication initiation by machine learning algorithms. Further prospective studies with large patient numbers are needed to create scoring systems as well as for their validation.Item Utilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Study(2022) Aydogan Duman, Ebru; Sagiroglu, Seref; Celtikci, Pinar; Demirezen, Mustafa Umut; Borcek, Alp Ozgun; Emmez, Hakan; Celtikci, Emrah; 34542897AIM: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation at the same time. MATERIAL and METHODS: MRIs of 300 healthy subjects were employed as training set. Additionally, test data were consisting anonymized T2-weigted MRIs of 27 healthy subjects and 27 HGG patients. Consecutive axial T2-weigted MRI slices of every subject were extracted and resized to 364x448 pixel resolution. The generative model produced random normal synthetic images and used these images for calculating residual loss to measure visual similarity between input MRIs and generated MRIs. RESULTS: The model correctly detected anomalies on 24 of 27 HGG patients' MRIs and marked them as abnormal. Besides, 25 of 27 healthy subjects' MRIs in the test dataset detected correctly as healthy MRI. The accuracy, precision, recall, and AUC were 0.907, 0.892, 0.923, and 0.907, respectively. CONCLUSION: Our proposed model demonstrates acceptable results can be achieved only by training with normal subject MRIs via using DCGAN model. This model is unique because it learns only from normal MRIs and it is able to find any abnormality which is different than the normal pattern.Item Characterization of Responder Profiles for Cardiac Resynchronization Therapy through Unsupervised Clustering of Clinical and Strain Data(2021) Gallard, Alban; Bidaut, Auriane; Hubert, Arnaud; Sade, Elif; Marechaux, Sylvestre; Sitges, Martha; Separovic-Hanzevacki, Jadranka; Le Rolle, Virginie; Galli, Elena; Hernandez, Alfredo; 33524492Background: The mechanisms of improvement of left ventricular (LV) function with cardiac resynchronization therapy (CRT) are not yet elucidated. The aim of this study was to characterize CRT responder profiles through clustering analysis, on the basis of clinical and echocardiographic preimplantation data, integrating automatic quantification of longitudinal strain signals. Methods: This was a multicenter observational study of 250 patients with chronic heart failure evaluated before CRT device implantation and followed up to 4 years. Clinical, electrocardiographic, and echocardiographic data were collected. Regional longitudinal strain signals were also analyzed with custom-made algorithms in addition to existing approaches, including myocardial work indices. Response was defined as a decrease of $15% in LV end-systolic volume. Death and hospitalization for heart failure at 4 years were considered adverse events. Seventy features were analyzed using a clustering approach (k-means clustering). Results: Five clusters were identified, with response rates between 50% in cluster 1 and 92.7% in cluster 5. These five clusters differed mainly by the characteristics of LV mechanics, evaluated using strain integrals. There was a significant difference in event-free survival at 4 years between cluster 1 and the other clusters. The quantitative analysis of strain curves, especially in the lateral wall, was more discriminative than apical rocking, septal flash, or myocardial work in most phenogroups. Conclusions: Five clusters are described, defining groups of below-average to excellent responders to CRT. These clusters demonstrate the complexity of LV mechanics and prediction of response to CRT. Automatic quantitative analysis of longitudinal strain curves appears to be a promising tool to improve the understanding of LV mechanics, patient characterization, and selection for CRT.Item Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features(2020) Altinkaynak, Miray; Dolu, Nazan; Guven, Aysegul; Pektas, Ferhat; Ozmen, Sevgi; Demirci, Esra; Izzetoglu, Meltem; 0000-0002-3104-7587; AAG-4494-2019The aim of this study was to build a machine learning model to discriminate Attention Deficit Hyperactivity Disorder (ADHD) patients and healthy controls using information from both time and frequency analysis of Event Related Potentials (ERP) obtained from Electroencephalography (EEG) signals while participants performed an auditory oddball task. The study included 23 unmedicated ADHD patients and 23 healthy controls. The EEG signal was analyzed in time domain by nonlinear brain dynamics and morphological features, and in time-frequency domain with wavelet coefficients. Selected features were applied to various machine learning techniques including; Multilayer Perceptron, Naive Bayes, Support Vector Machines, k-nearest neighbor, Adaptive Boosting, Logistic Regression and Random Forest to classify ADHD patients and healthy controls. Longer P300 latencies and smaller P300 amplitudes were observed in ADHD patients relative to controls. In fractal dimension calculation relative to the control group, the ADHD group demonstrated reduced complexity. In addition, certain wavelet coefficients provided significantly different values in both groups. Combining these extracted features, our results indicated that Multilayer Perceptron method provided the best classification with an accuracy rate of 91.3% and a high level of reliability of concurrence (Kappa = 0.82). The results showed that combining time and frequency domain features can be a useful and discriminative for diagnostic purposes in ADHD. The study presents a supporting diagnostic tool that uses EEG signal processing and machine learning algorithms. The findings would be helpful in the objective diagnosis of ADHD. (C) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.