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Purpose: The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from. Methods: A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly. Results: The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application. Conclusion: The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future. [ABSTRACT FROM AUTHOR]

Working in a hospital is a very stressful and sometimes risky process for all healthcare professionals who have experienced this difficult process for years. This situation may increase the suicidal tendency of healthcare workers as a result of not only physical but also psychological exhaustion. The purpose of this study was to determine the examination of suicidal ideation and related factors among healthcare professionals working in a research and practice hospital. The data of this descriptive and cross-sectional study were collected prospectively by clinicians in a research and practice hospital. The sample of the research consisted of n=600 healthcare workers. The average age of healthcare workers was 32.46±7.79 years; 48% were women and 47.7% were married. 4% of healthcare workers had a past suicide attempt, and 18% had various levels of suicidal ideation. Women were more likely to attempt suicide. There was a positive, strong, and statistically significant correlation between depression, anxiety severity and suicidal ideation. Suicidal thoughts of healthcare professionals are affected by gender, depression and anxiety. The mental health of healthcare professionals should be evaluated periodically, and psychological support should be provided to healthcare professionals at risk of suicide. [ABSTRACT FROM AUTHOR]