Meslek Yüksek Okulları / Vocational Schools

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    Detecting COVID-19 from Respiratory Sound Recordings with Transformers
    (2022) Aytekin, Idil; Dalmaz, Onat; Ankishan, Haydar; Saritas, Emine U.; Bagci, Ulas; Cukur, Tolga; Celik, Haydar; Drukker, K; Iftekharuddin, KM
    Auscultation is an established technique in clinical assessment of symptoms for respiratory disorders. Auscultation is safe and inexpensive, but requires expertise to diagnose a disease using a stethoscope during hospital or office visits. However, some clinical scenarios require continuous monitoring and automated analysis of respiratory sounds to pre-screen and monitor diseases, such as the rapidly spreading COVID-19. Recent studies suggest that audio recordings of bodily sounds captured by mobile devices might carry features helpful to distinguish patients with COVID-19 from healthy controls. Here, we propose a novel deep learning technique to automatically detect COVID-19 patients based on brief audio recordings of their cough and breathing sounds. The proposed technique first extracts spectrogram features of respiratory recordings, and then classifies disease state via a hierarchical vision transformer architecture. Demonstrations are provided on a crowdsourced database of respiratory sounds from COVID-19 patients and healthy controls. The proposed transformer model is compared against alternative methods based on state-of-the-art convolutional and transformer architectures, as well as traditional machine-learning classifiers. Our results indicate that the proposed model achieves on par or superior performance to competing methods. In particular, the proposed technique can distinguish COVID-19 patients from healthy subjects with over 94% AUC.
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    Factors Affecting Nurses' Professional Commitment During the COVID-19 Pandemic: A Cross-Sectional Study
    (2021) Duran, Secil; Celik, Isa; Ertugrul, Bekir; Ok, Serife; Albayrak, Sevil; 33794061
    Aim This study aims to investigate the factors affecting nurses' professional commitment during the COVID-19 pandemic. Background Commitment to a profession requires doing the best for that profession. In the case of the nursing profession, professional commitment gains greater importance in times of crisis, like the COVID-19 pandemic. Methods This cross-sectional study was conducted with 389 nurses in the Turkish state hospital. Institutional permissions, ethical approval and written consents from the participants were obtained before carrying out the study. Results The participants' mean Perceived Organizational Obstruction Scale score was 20.07 +/- 8.06 (min = 5.0, max = 35.0), and their mean Nursing Professional Commitment Scale score was 71.20 +/- 11.94 (min = 30.0, max = 103.0). Socio-demographic variables and perception of organisational obstruction predicted 36.7% of the variance in the professional commitment (p < .001). Conclusion A road map based on the study results was developed for hospitals and nurse managers to maintain and increase nurses' professional commitment. Implications for Nursing Management The results of this study may help institutions and nurse managers understand the factors affecting professional commitment during the pandemic as a whole, as well as determine primary strategies based on the importance of these factors.