Towards Suicide Prevention from Bipolar Disorder with Temporal
  Symptom-Aware Multitask Learning
Daeun Lee Sejung Son Hyolim Jeon Seungbae Kim Jinyoung Han
Bipolar disorder (BD) is closely associated with an increased risk of
suicide. However, while the prior work has revealed valuable insight into
understanding the behavior of BD patients on social media, little attention has
been paid to developing a model that can predict the future suicidality of a BD
patient. Therefore, this study proposes a multi-task learning model for
predicting the future suicidality of BD patients by jointly learning current
symptoms. We build a novel BD dataset clinically validated by psychiatrists,
including 14 years of posts on bipolar-related subreddits written by 818 BD
patients, along with the annotations of future suicidality and BD symptoms. We
also suggest a temporal symptom-aware attention mechanism to determine which
symptoms are the most influential for predicting future suicidality over time
through a sequence of BD posts. Our experiments demonstrate that the proposed
model outperforms the state-of-the-art models in both BD symptom identification
and future suicidality prediction tasks. In addition, the proposed temporal
symptom-aware attention provides interpretable attention weights, helping
clinicians to apprehend BD patients more comprehensively and to provide timely
intervention by tracking mental state progression.