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.
@article{arxiv.1712.03538,
title = {Multi-Task Learning for Mental Health using Social Media Text},
author = {Adrian Benton and Margaret Mitchell and Dirk Hovy},
journal= {arXiv preprint arXiv:1712.03538},
year = {2017}
}