Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning

TL;DR

This paper introduces a novel multi-task learning model with temporal symptom-aware attention to predict future suicidality in bipolar disorder patients using social media data, aiding early intervention and clinical understanding.

Contribution

It presents a new clinically validated dataset and a temporal attention mechanism that improves prediction accuracy and interpretability for bipolar disorder-related suicidality.

Findings

Outperforms state-of-the-art models in prediction tasks

Provides interpretable attention weights for clinical insights

Builds a large, validated dataset of bipolar disorder social media posts

Abstract

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.