Gradually evaluating of suicidal risk in depression by semi-supervised cluster analysis on resting-state fMRI

• Published in: Brain imaging and behavior Vol. 15; no. 4; pp. 2149 - 2158
• Main Authors: Dai, Zhongpeng, Shen, Xiangyu, Tian, Shui, Yan, Rui, Wang, Huan, Wang, Xinyi, Yao, ZhiJian, Lu, Qing
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2021; Springer Nature B.V
• Subjects: Biomedical and Life Sciences; Biomedicine; Circuits; Cluster analysis; Data analysis; Evaluation; Functional magnetic resonance imaging; Iterative methods; Learning algorithms; Machine learning; Mental depression; Neuropsychology; Neuroradiology; Neurosciences; Original Research; Patients; Psychiatry; Risk assessment; Signs and symptoms; Suicide; Suicides & suicide attempts; fMRI; Depression; Semi-supervised model; Suicide

A timely and effective evaluation of the suicidal ideation bears practical meaning, particularly for the depressive who tend to disguise the real suicide intent and without obvious symptoms. Measuring individual ideation of the depression with uncertain or transient suicide crisis is the purpose. Re...