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Jae Myeong Kang,1,* Seong-Jin Cho,1,* Seo-Eun Cho,1 Taemo Bang,2 Byung Do Chae,3 Eojin Yi,4 Seung Min Bae,1 Kyoung-Sae Na,1 Jaehun Jung,5 Seung-Gul Kang1 1Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea; 2Artificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea; 3Unit of Frontier Exploration, Korea Polar Research Institute, Incheon, Republic of Korea; 4Department of Plastic and Reconstructive Surgery, Yonsei University College of Medicine, Seoul, Republic of Korea; 5Department of Preventive Medicine, Gachon University College of Medicine, Incheon, Republic of Korea*These authors contributed equally to this workCorrespondence: Seung-Gul Kang, Department of Psychiatry, Gil Medical Center, Gachon University, College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea, Tel +82-32-468-9932, Fax +82-32-468-9962, Email kangsg@gachon.ac.krPurpose: Antarctica is a region with extreme climate, characterized by extreme cold and photoperiod. No research has been conducted on the mental health of Korean Antarctic dispatchers. The aim of this study was to investigate the status of mental illness and changes in mood and sleep among Korean crew members staying for a long-term period in the Antarctic station.Methods: From 2017 to 2020, crew members who were dispatched from South Korea to two Antarctic stations for a one-year period participated in this study. The crew were evaluated for mood and sleep status and mental illness through psychological tests and interviews by board-certified psychiatrists once before departure and twice during their stay in Antarctica. The incidence of mental illness was confirmed and changes in sleep and depression were analyzed.Results: A total of 88 participants were included in the final analysis, and 7 of them (8.0%) were diagnosed with me

Kyoung-Sae Na,1 Zong Woo Geem,2 Seo-Eun Cho3 1Department of Psychiatry, Gachon University College of Medicine, Incheon, 21565, Republic of Korea; 2College of IT Convergence, Gachon University, Seongnam, 13120, Republic of Korea; 3Department of Psychiatry, Gil Medical Center, Incheon, 21565, Republic of KoreaCorrespondence: Seo-Eun ChoDepartment of Psychiatry, Gil Medical Center, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea, Tel +82-32-460-3879, Fax +82-32-466-3267, Email arztin01@gilhospital.comPurpose: Suicide is an important health and social concern worldwide. Both suicidal ideation and suicide rates are higher in the elderly population than in other age groups; thus, more careful attention and targeted interventions are required. Therefore, we have developed a model to predict suicidal ideation in the community-dwelling elderly aged of > 55 years.Patients and Methods: A random forest algorithm was applied to those who participated in the Korea Welfare Panel. We used a total of 26 variables as potential predictors. To resolve the imbalance in the dataset resulting from the low frequency of suicidal ideation, training was performed by applying the synthetic minority oversampling technique. The performance index was calculated by applying the predictive model to the test set, which was not included in the training process.Results: A total of 6410 elderly Korean aged of > 55 (mean, 71.48; standard deviation, 9.56) years were included in the analysis, of which 2.7% had suicidal ideation. The results for predicting suicidal ideation using the 26 chosen variables showed an AUC of 0.879, accuracy of 0.871, sensitivity of 0.750, and specificity of 0.874. The most significant variable in the predictive model was the severity of depression, followed by life satisfaction and self-esteem factors. Basic demographic variables such as age and gender demonstrated a relatively small effect.Conclusion: Machine learning can be used to create algor