Graph learning based suicidal ideation detection via tree-drawing test

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.

The proposed method first constructs a semantic graph based on psychological features annotated automatically from tree-drawing images, and leverages a Graph Convolutional Network (GCN) model to realize individual suicidal ideation detection. To evaluate this method, a real dataset of 806 students from primary and secondary school in Shaanxi Province, China, is collected, and some metrics including macro-F1, G-mean, and false positive rate are used.

The results demonstrate that the proposed method significantly outperforms traditional machine learning and convolution neural network approaches. The ablation study demonstrates the effectiveness of feature "leaves and fruits" in detecting suicidal ideation. Further experiments demonstrate that the proposed method remains stable even when the model is disturbed, such as when a tree-drawing image cannot be fully represented.

The proposed method highlights its effectiveness in large-scale suicidal ideation screening, as it not only achieves high detection performance but also maintains model stability while remaining flexible and adaptable.