  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 multi-task 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.
