Browsing by Author "Saygili, Gorkem"

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    The İmportance of Mentalization, Coping Mechanisms, and Perceived Stress in The Prediction of Resilience of Healthcare Workers
    (2023) Alici, Yasemin Hosgoren; Hasanli, Jamal; Saygili, Gorkem; Kocak, Orhan Murat; 36217606
    Resilience is the process of overcoming stressors. Being able to examine the effect of the Covid epidemic on healthcare workers (HCWs) has provided us a unique opportunity to understand the impact of trauma on resilience. We aimed to investigate the relationship between stress, mentalization, and an individual's coping capacity against a real risk (Covid-19) and evaluate the predictors of resilience. 302 HCWs have enrolled in the study and completed an online questionnaire assessing demographics, perceived stress, resilience, coping, and mentalization. We utilized statistical analysis together with a Random Forest classifier to analyze the interaction between these factors extensively. We applied ten times ten-fold cross-validation and plotted Receiver Operator Characteristic (ROC) with the calculated Area Under the Curve(AUC) score and identify the most important features. Our experiments showed that the Perceived stress scale has the strongest relationship with resilience. The subject's awareness level of emotional states is an important factor that determines the level of resilience. Coping styles such as the decision of giving up is also a crucial indicator. We conclude that being aware of the risks and the mental states are the dominant factors behind the resilience levels of healthcare workers under pandemic conditions.
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    An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry
    (2021) Guney, Gokhan; Yigin, Busra Ozgode; Guven, Necdet; Colak, Burcin; Alici, Yasemin Hosgoren; Erzin, Gamze; Saygili, Gorkem; 0000-0003-3384-8131; 33888650
    Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, estimating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.