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왕정현,박철수,김봉조,이철순,차보석,이소진,이동윤,서지영,안인영,백종철,강형석,문성호,Wang, Jung-Hyun,Park, Chul-Soo,Kim, Bong-Jo,Lee, Cheol-Soon,Cha, Boseok,Lee, So-Jin,Lee, Dongyun,Seo, Ji-Yeong,Ahn, InYoung,Baek, Jong Chul,Kang, Hyung 한국정신신체의학회 2015 정신신체의학 Vol.23 No.2
연구목적 결핵환자 중 우울증 고위험 환자와 저위험 환자의 비교연구를 통해 결핵환자의 우울증 위험요인을 밝히고자 했다. 방 법 57명의 결핵환자를 대상으로 벡 우울 검사 2판을 이용하여 우울증상을 평가하였다. 우울증 고위험군과 저위험군으로 나누어 이분형로지스틱회귀분석 및 계산도표를 작성하였다. 결 과 신체비만지수가 낮아질수록 우울증 고위험군에 속할 위험은 높았다. 결핵치료 임의중단력이 있을 경우 우울증 고위험군에 속할 위험은 6배 높았다. 우울증 과거병력이 있는 경우 우울증 고위험군에 속할 위험은 25배 높았다. Original C-index는 0.789였고 bias corrected C-index는 0.754로 상당한 일치를 보였다. 결 론 낮은 신체비만지수, 결핵치료 임의중단력, 우울증 과거병력은 결핵환자의 우울증 위험요인임을 밝혔다. 이는 결핵환자에 대한 정신건강의학과적 개입 및 치료를 위한 근거자료가 될 것이다. Objectives : This study aimed to investigate the risk factors of depression for patients with tuberculosis(TB). Methods : A total of 57 patients with TB were recruited. All participants completed the Becks Depression Inventory-II for evaluating depressive symptoms. The risk factor for depression was analyzed by binary logistic regression analysis. Nomogram was performed for probability of depression. Results : Low body mass index(BMI, OR 0.801, 95% CI 0.65, 0.98), interruption of treatment for TB(OR 5.908, 95% CI 1.19, 29.41), past history of depression(OR 24.653, 95% CI 1.99, 308.44) were associated with increased risk for depression. The calibration curve for predicting probability of survival showed a good agreement between the nomogram and actual observation(Original C-index=0.789, bias corrected C-index=0.754). Conclusions : The result of the present study indicate that low BMI, interruption of treatment for TB, and past history of depression were risk factors for depression in patients with TB. The psychiatric intervention may be needed to prevent depression if the patients with TB have risk factor during treatment for TB.
왕정현,김진환 한국로봇학회 2017 로봇학회 논문지 Vol.12 No.3
This paper proposes a method to segment urban scenes semantically based on location prior information. Since major scene elements in urban environments such as roads, buildings, and vehicles are often located at specific locations, using the location prior information of these elements can improve the segmentation performance. The location priors are defined in special 2D coordinates, referred to as road-normal coordinates, which are perpendicular to the orientation of the road. With the help of depth information to each element, all the possible pixels in the image are projected into these coordinates and the learned prior information is applied to those pixels. The proposed location prior can be modeled by defining a unary potential of a conditional random field (CRF) as a sum of two sub-potentials: an appearance feature-based potential and a location potential. The proposed method was validated using publicly available KITTI dataset, which has urban images and corresponding 3D depth measurements.