Meslek Yüksek Okulları / Vocational Schools

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    Home Application by Hemodialysis Patients for Hypertension Management
    (2017) Gokdogan, Feray; Kes, Duygu; Turgay, Gulay; Tuna, Dondu
    OBJECTIVE: This study aimed to determine home application by hemodialysis patients for hypertension management. MATERIAL and METHODS: The population of the descriptive study included a total of 279 patients who were treated at the hemodialysis centers of two state hospitals, one private hospital and one training and research hospital situated within Karabuk province. A total of 120 patients who were over 18 years of age, had hypertension, could communicate, and whose clinic state were stable, who did not have any mental and psychiatric disorder and who accepted to participate in the research voluntarily were included in the sample. RESULTS: It was determined that 59.2% of the patients who participated in the study did not measure their blood pressures at home regularly; 44.6% did not take their medication regularly and did not know the name and dosages of their medication (60.7% and 64.3% respectively); 73.2% had stopped taking medication without the physician's knowledge; 85% used salt in meals; and 70.8% and 46.7% respectively did not comply with the liquid limitation. CONCLUSION: It is important to reveal the effects of a nursing care approach for supporting hypertension self-management at home of our patients based on their individual characteristics through studies focusing on regular training, monitoring and providing consultancy services.
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    Blood pressure prediction from speech recordings
    (2020) Ankishan, Haydar
    The aim of this study is to extract new features to show the relationship between speech recordings and blood pressure (BP). For this purpose, a database consisting of / a / vowels with different BP values under the same room and environment conditions is presented to the literature. Convolutional Neural Networks- Regression (CNN-R), Support Vector Machines- Regression (SVMs-R) and Multi Linear Regression (MLR) are used in this study to predict BP with extracted features. From the experiments, the highest accuracy rates of BP prediction from / a / vowel have been obtained based on Systolic BP values with CNNR. In the study, 89.43 % for MLR, 92.15 % for SVM-R and 93.65 % for CNN-R are obtained when ReliefF has been used. When the root mean square errors (RMSE) are considered, the lowest error value is obtained with CNN-R as RMSE = 0.2355. In conclusion, it can be observed that the proposed feature vector (FVx) shows a relationship between BP and the human voices, and in this direction, it can be used as an FVx in a system that will be developed in order to follow the tension of individuals. (C) 2020 Elsevier Ltd. All rights reserved.