Semi-supervised Graph Instance Transformer for Mental Health Inference

Abstract

Mental health disorders, such as generalized anxiety disorder and depression, are prevalent in modern society. Early detection of mental illness is essential to minimize the negative consequences of long-term psychological discomfort and behavioral dysfunction. As a diverse set of embedded sensors in smart mobile devices becomes commonplace, passively and continuously collected mobile sensing data are increasingly being used to develop machine learning based tools for early-stage disease diagnosis. In the training process of machine learning models, self-reported results from ecological momentary assessments (EMAs) are usually employed to provide supervisions. However, complete responses of these high-frequency surveys in the wild are impractical due to heavy user burden and low user engagement. Without the availability of EMA responses in low level of label granularity, the annotations in the high level can only provide weak supervisions. To leverage the vast majority of unannotated data in different levels of granularity, in this paper, we propose an end-to-end graph neural network algorithm called semi-supervised Graph Instance Transformer (SS-GIT) based on multiple instance learning and contrastive self-supervised learning to predict early signs of generalized anxiety disorder and depression under the weak supervisions. Using a mobile sensing dataset that we collected from around 1,300 participants in the wild, our empirical results demonstrate improved performance when compared to the existing state-of-the-art baseline graph neural networks for mental health inference. On average, our proposed model outperforms the best baseline model by 8.8% on Fl-score, 6.7% on ROC-AUC, and 7.2% on PR-AUC.