Prediction of acute suicidal ideation in young adults using multi-dimensional scales: A graph neural network approach

Abstract

Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as psychiatric well-being during COVID-19 crisis. Using questionnaires is an alternative to labour-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffer from low sensitivity. We developed and validated a graph neural network model, MindWatchNet, which increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training; n = 14,238 for testing) using multi-dimensional questionnaires and suicidal ideation within 2 weeks as the prediction target. MindWatchNet achieved the highest sensitivity of 80.9% and an area under curve of 0.877 (95% confidence interval, 0.854–0.897). We demonstrated that multi-dimensional deep features covering depression, anxiety, resilience, self-esteem, and clinico-demographic information contribute to SI prediction. MindWatchNet might be useful in the remote evaluation of suicide risk in the general population of young adults for specific situations such as the COVID-19 pandemic.