Graph Learning Based Suicidal Ideation Detection via Tree-Drawing Test

Ye Liu¹Jiashuo Zheng¹Yang Zeng²Fang Luo²Xuetao Tian^2*

• ¹School of Future Technology, South China University of Technology, Guangzhou, Guangdong Province, China
• ²Faculty of Psychology, Beijing Normal University, Beijing, China

Adolescent suicide is a critical public health concern worldwide, necessitating effective methods for early detection of high suicidal ideation. Traditional detection methods, such as self-report scales, suffer from limited accuracy and are susceptible to personal concealment. Automatic methods based on artificial intelligence techniques are more accurate, while they often lack scalability due to strict data requirements. In order to achieve a balance between accuracy and scalability, this paper introduces the Tree-Drawing Test (TDT) as an effective tool for suicidal ideation detection, and proposes a novel graph learning approach to enable its automatic application. Specifically, we first construct a semantic graph based on psychological features annotated automatically from tree drawing images, and then a Graph Convolutional Network (GCN) model is trained to realize individual suicidal ideation detection. The proposed method is evaluated on a collected real dataset of 806 students from primary and secondary school in Shaanxi Province, China. Our results demonstrate that the proposed method significantly outperforms traditional machine learning and convolution neural network approaches, highlighting its effectiveness in large-scale suicidal ideation screening.