  Suicide is the 10th leading cause of death in the U.S (1999-2019). However,
predicting when someone will attempt suicide has been nearly impossible. In the
modern world, many individuals suffering from mental illness seek emotional
support and advice on well-known and easily-accessible social media platforms
such as Reddit. While prior artificial intelligence research has demonstrated
the ability to extract valuable information from social media on suicidal
thoughts and behaviors, these efforts have not considered both severity and
temporality of risk. The insights made possible by access to such data have
enormous clinical potential - most dramatically envisioned as a trigger to
employ timely and targeted interventions (i.e., voluntary and involuntary
psychiatric hospitalization) to save lives. In this work, we address this
knowledge gap by developing deep learning algorithms to assess suicide risk in
terms of severity and temporality from Reddit data based on the Columbia
Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep
learning approaches: time-variant and time-invariant modeling, for user-level
suicide risk assessment, and evaluate their performance against a
clinician-adjudicated gold standard Reddit corpus annotated based on the
C-SSRS. Our results suggest that the time-variant approach outperforms the
time-invariant method in the assessment of suicide-related ideations and
supportive behaviors (AUC:0.78), while the time-invariant model performed
better in predicting suicide-related behaviors and suicide attempt (AUC:0.64).
The proposed approach can be integrated with clinical diagnostic interviews for
improving suicide risk assessments.
