Multitask Learning for Mental Health Conditions with Limited Social Media Data

Adrian Benton, Margaret Mitchell, Dirk Hovy
2017 Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers  
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
more » ... e 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.
doi:10.18653/v1/e17-1015 dblp:conf/eacl/HovyMB17 fatcat:s6x5ylro3zfmpizr4hrof5ynl4