We introduce initial groundwork for estimating suicide risk and mental health in a deep learning framework. By modeling multiple conditions, the system learns to make predictions about suicide risk and mental health at a low false positive rate. Conditions are modeled as tasks in a multitask learning (MTL) framework, with gender prediction as an additional auxiliary task. We demonstrate the effectiveness of multi-task learning by comparison to a well-tuned single-task baseline with the same number of parameters. Our best MTL model predicts potential suicide attempt, as well as the presence of atypical mental health, with AUC > 0.8. We also find additional large improvements using multi-task learning on mental health tasks with limited training data.
Bentoni et al. REF demonstrated the effectiveness of multi-task learning (MTL) models on mental health disorders with a limited amount of target data.
Multi-Task Learning for Mental Health using Social Media Text
Proceedings of the 15th Conference of the EACL (2017) 152-162
Computer Science