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Publication Metadata only Screening of anxiety and quality of life in people with epilepsy(W B SAUNDERS CO LTD, 2017) Gur-Ozmen, Selen; Leibetseder, Annette; Cock, Hannah R.; Agrawal, Niruj; von Oertzen, Tim J.; City St Georges, University of London; Bahcesehir University; City St Georges, University of LondonPurpose: Up to 60% of people with epilepsy (PwE) have psychiatric comorbidity including anxiety. Anxiety remains under recognized in PwE. This study investigates if screening tools validated for depression could be used to detect anxiety disorders in PWE. Additionally it analyses the effect of anxiety on QoL. Method: 261 participants with a confirmed diagnosis of epilepsy were included. Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) and Emotional Thermometers (ET), both validated to screen for depression were used. Hospital Anxiety and Depression Scale-Anxiety (HADS-A) with a cut off for moderate and severe anxiety was used as the reference standard. QoL was measured with EQ5-D. Sensitivity, specificity, positive and negative predictive value and ROC analysis as well as multivariate regression analysis were performed. Results: Patients with depression (n=46) were excluded as multivariate regression analysis showed that depression was the only significant determinant of having anxiety in the group. Against HADS-A, NDDI-E and ET-7 showed highest level of accuracy in recognizing anxiety with ET7 being the most effective tool. QoL was significantly reduced in PwE and anxiety. Conclusion: Our study showed that reliable screening for moderate to severe anxiety in PwE without co-morbid depression is feasible with screening tools for depression. The cut off values for anxiety are different from those for depression in ET7 but very similar in NDDI-E. ET7 can be applied to screen simultaneously for depression and pure anxiety. Anxiety reduces significantly QoL. We recommend screening as an initial first step to rule out patients who are unlikely to have anxiety. (C) 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.Publication Metadata only Prediction of cyclosporine A blood levels: An application of the adaptive-network-based fuzzy inference system (ANFIS) in assisting drug therapy(2008) Gören, Sezer; Karahoca, Adem; Onat, Filiz Yilmaz; Gören, Mehmet Zafer; Gören, Sezer, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Karahoca, Adem, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Onat, Filiz Yilmaz, Department of Pharmacology and Clinical Pharmacology, Marmara Üniversitesi Tip Fakültesi, Istanbul, Turkey; Gören, Mehmet Zafer, Department of Pharmacology and Clinical Pharmacology, Marmara Üniversitesi Tip Fakültesi, Istanbul, TurkeyObjective: Therapeutic drug monitoring (TDM) is a procedure in which the levels of drugs are assayed in various body fluids with the aim of individualizing the dose of critical drugs, such as cyclosporine A. Cyclosporine A assays are performed in blood. Methods: We proposed the use of the Takagi and Sugeno-type adaptive-network-based fuzzy inference system (ANFIS) to predict the concentration of cyclosporine A in blood samples taken from renal transplantation patients. We implemented the ANFIS model using TDM data collected from 138 patients and 20 input parameters. Input parameters for the model consisted of concurrent use of drugs, blood levels, sampling time, age, gender, and dosing intervals. Results: Fuzzy modeling produced eight rules. The developed ANFIS model exhibited a root mean square error (RMSE) of 0.045 with respect to the training data and an error of 0.057 with respect to the checking data in the MATLAB environment. Conclusion: ANFIS can effectively assist physicians in choosing best therapeutic drug dose in the clinical setting. © 2008 Springer-Verlag. © 2009 Elsevier B.V., All rights reserved., MEDLINE® is the source for the MeSH terms of this document.Publication Metadata only Caregiving style, problem solving strategies, anger style and health status in women caregivers of alzheimer patients and healthy elderly, Alzhei̇mer hastalarina ve saǧlikli yaşlilara bakimveren kadin yakinlarda bakim verme tarzi, problem çözme strateji̇leri̇,öfke tarzlari ve saǧlik durumunun i̇ncelenmeṡi(2009) Korkut, Yeflim; Sertel-Berk, Hanife Özlem; Korkut, Yeflim, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sertel-Berk, Hanife Özlem, Psikoloji Anabilim Dali, Istanbul Üniversitesi, Istanbul, TurkeyIntroduction: The principle aim of this study is to investigate the caregiving style of the participants and the kind of support they receive, to compare anger levels, problem solving approaches and health problems of Alzheimer Disease (AD) patient female caregivers with a control group. This study further examines the effect of group, age, health status, anger style and problem solving style on depression. Materials and Method: 42 female caregivers (22 AD and 20 control) participated in the study. They were given a demographic questionnaire, Beck Depression Inventory-BDI, State-Trait Anger Scale-STAS and Problem Solving Inventory-PSI. Results: Results indicated that both AD and control group caregivers were similar in terms of caregiving style. Though AD and control group caregivers did not differ in terms PSI and BDI, there were significant differences between those who reported illness and those who did not in terms of impatient and thoughtful approach sub-tests of PSI. The regression analysis showed that age and repressed anger significantly explained the variation in BDI. Conclusion: Altogether results showed that in a relatively low at risk women caregivers group, regardless of being AD caregiver or not, age and repressed anger are important factors on depression levels. Health status of caregiver seems to be a determining factor on PSI. © 2009 Elsevier B.V., All rights reserved.Publication Metadata only Information system design for a hospital emergency department: A usability analysis of software prototypes(2010) Karahoca, Adem; Bayraktar, Erkan; Tatoglu, Ekrem; Karahoca, Dilek Yiğit; Karahoca, Adem, Faculty of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Bayraktar, Erkan, Faculty of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Tatoglu, Ekrem, Faculty of Economics and Administrative Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Karahoca, Dilek Yiğit, Faculty of Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyStudy objective: The purpose of this study is to evaluate the usability of emergency department (ED) software prototypes developed for Tablet personal computers (Tablet PCs) in order to keep electronic health records (EHRs) of patients errorless and accessible through mobile technologies. In order to serve this purpose, two alternative prototypes were developed for Tablet PCs: Mobile Emergency Department Software (MEDS) and Mobile Emergency Department Software Iconic (MEDSI) among which the user might choose the more appropriate one for ED operations based on a usability analysis involving the target users. Methods: The study is based on a case study of 32 potential users of our prototypes at the ED of Kadikoy-AHG in Istanbul, Turkey. We examined usability of the prototypes for medical information systems by means of Nielsen's heuristic evaluation and cognitive walkthrough methods relying on 7-point scales, and scenario completion success rate and average scenario completion time, respectively. Results: The implementation of MEDSI in our case study confirmed the view that the usability evaluation results of iconic GUIs were better than those of non-iconic GUIs in terms of Nielsen's heuristic evaluation, effectiveness and user satisfaction. For the whole sample, paired t-test scores indicated that there was a significant difference (p < 0.01) between mean values of Nielsen's usability scores toward MEDS and MEDSI indicating that MEDSI was evaluated more favorably than MEDS. As for effectiveness of the prototypes, significant differences (p < 0.01) were noted between MEDS and MEDSI in terms of both overall scenario completion success rate and average scenario completion time. Similarly, for the full sample of users independent sample t-test scores indicated that MEDSI was perceived significantly more favorable (p < 0.01) than MEDS in terms of overall user satisfaction. Conclusion: The study provides two important contributions to the extant literature. First, it addresses a topic and methodology that serves potentially interesting to the biomedical informatics community. Drawing on good background information and appropriate context, it involves various aspects of usability testing. Another contribution of the study lies in its examination of two different prototypes during the design phase involving the target users. © 2009 Elsevier Inc. All rights reserved. © 2010 Elsevier B.V., All rights reserved., MEDLINE® is the source for the MeSH terms of this document.Publication Metadata only Classification of the colonic polyps in CT-colonography using region covariance as descriptor features of suspicious regions(2010) Kılıç, Niyazi; Kursun, Olcay; Uçan, Osman Nuri; Kılıç, Niyazi, Department of Electrical and Electronics Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Department of Electrical and Electronics Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Uçan, Osman Nuri, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyWe present an algorithm to classify polyps in CT colonography images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, they cannot simply be fed to traditional machine learning tools such as support vector machines (SVMs) or artificial neural networks (ANNs). To benefit from the simple yet one of the most powerful nonparametric machine learning approach k-nearest neighbor classifier, it suffices to compute the pairwise distances among the covariance descriptors using a distance metric involving their generalized eigenvalues, which also follows from the Lie group structure of positive definite matrices. This approach is fast and discriminates polyps from non-polyps with high accuracy using only a small size descriptor, which consists of 36 unique features per image region extracted from the suspicious regions that we have obtained by combined cellular neural network (CNN) and template matching detection method. These suspicious regions are, in average, 15 × 17 = 255 pixels in our experiments. © Springer Science + Business Media, LLC 2008. © 2010 Elsevier B.V., All rights reserved., MEDLINE® is the source for the MeSH terms of this document.Publication Metadata only Telediagnosis of parkinson's disease using measurements of dysphonia(2010) Sakar, C. Okan; Kursun, Olcay; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyParkinson's disease (PD) is a neurological illness which impairs motor skills, speech, and other functions such as mood, behavior, thinking, and sensation. It causes vocal impairment for approximately 90% of the patients. As the symptoms of PD occur gradually and mostly targeting the elderly people for whom physical visits to the clinic are inconvenient and costly, telemonitoring of the disease using measurements of dysphonia (vocal features) has a vital role in its early diagnosis. Such dysphonia features extracted from the voice come in variety and most of them are interrelated. The purpose of this study is twofold: (1) to select a minimal subset of features with maximal joint relevance to the PD-score, a binary score indicating whether or not the sample belongs to a person with PD, and (2) to build a predictive model with minimal bias (i.e. to maximize the generalization of the predictions so as to perform well with unseen test examples). For these tasks, we apply the mutual information measure with the permutation test for assessing the relevance and the statistical significance of the relations between the features and the PD-score, rank the features according to the maximum-relevance-minimum-redundancy (mRMR) criterion, use a Support Vector Machine (SVM) for building a classification model and test it with a more suitable cross-validation scheme that we called leave-one-individual-out that fits with the dataset in hand better than the conventional bootstrapping or leave-one-out validation methods. © 2009 Springer Science+Business Media, LLC. © 2010 Elsevier B.V., All rights reserved., MEDLINE® is the source for the MeSH terms of this document.Publication Metadata only Coupled nonparametric shape and moment-based intershape pose priors for multiple basal ganglia structure segmentation(2010) Uzunbaş, Mustafa Gökhan; Soldea, Octavian; Ünay, Devrim; Çetin, Müjdat; Unal, Gozde Bozkurt; Erçil, Aytül; Ekin, Ahmet; Uzunbaş, Mustafa Gökhan, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey, Department of Computer Science, Piscataway, United States; Soldea, Octavian, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Ünay, Devrim, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Çetin, Müjdat, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Unal, Gozde Bozkurt, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Erçil, Aytül, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Ekin, Ahmet, Video Processing and Analysis Group, Philips Research, Eindhoven, NetherlandsThis paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance images. We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only Denoising embolic Doppler ultrasound signals using Dual Tree Complex Discrete Wavelet Transform(2010) Serbes, Görkem; Aydın, Nizamettin; Serbes, Görkem, Department of Electrical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyEarly and accurate detection of asymptomatic emboli is important for monitoring of preventive therapy in stroke-prone patients. One of the problems in detection of emboli is the identification of an embolic signal caused by very small emboli. The amplitude of the embolic signal may be so small that advanced processing methods are required to distinguish these signals from Doppler signals arising from red blood cells. In this study instead of conventional discrete wavelet transform, the Dual Tree Complex Discrete Wavelet Transform was used for denoising embolic signals. Performances of both approaches were compared. Unlike the conventional discrete wavelet transform discrete complex wavelet transform is a shift invariant transform with limited redundancy. Results demonstrate that the Dual Tree Complex Discrete Wavelet Transform based denoising outperforms conventional discrete wavelet denoising. Approximately 8 dB improvement is obtained by using the Dual Tree Complex Discrete Wavelet Transform compared to the improvement provided by the conventional Discrete Wavelet Transform (less than 5 dB). © 2010 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only The 2010 Eurobarometer on the life sciences(2011) Gaskell, George D.; Allansdottir, Agnes; Allum, Nick; Castro, Paula; Esmer, Yilmaz; Fischler, Claude; Jackson, Jonathan; Kronberger, Nicole; Hampel, Jürgen; Mejlgaard, Niels; Gaskell, George D., London School of Economics and Political Science, London, United Kingdom; Allansdottir, Agnes, Università degli Studi di Siena, Siena, Italy; Allum, Nick, University of Essex, Colchester, United Kingdom; Castro, Paula, Iscte – Instituto Universitário de Lisboa, Lisbon, Portugal; Esmer, Yilmaz, Bahçeşehir Üniversitesi, Istanbul, Turkey; Fischler, Claude, École des Hautes Études en Sciences Sociales, Paris, France; Jackson, Jonathan, London School of Economics and Political Science, London, United Kingdom; Kronberger, Nicole, Johannes Kepler University Linz, Linz, Austria; Hampel, Jürgen, Universität Stuttgart, Stuttgart, Germany; Mejlgaard, Niels, Aarhus Universitet, Aarhus, Denmark[No abstract available]Publication Metadata only Response to Commentary: 'Methodological concerns in usability evaluation of software prototypes' by Khajouei et al.(2011) Karahoca, Adem; Karahoca, Dilek Yiğit; Bayraktar, Erkan; Tatoglu, Ekrem; Karahoca, Adem, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Karahoca, Dilek Yiğit, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Bayraktar, Erkan, Department of Industrial Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Tatoglu, Ekrem, Faculty of Economics and Administrative Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey[No abstract available]