Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Abstract

Mental health illness such as depression is a significant risk factor for suicide ideation, behaviors, and attempts. A report by Substance Abuse and Mental Health Services Administration (SAMHSA) shows that 80% of the patients suffering from Borderline Personality Disorder (BPD) have suicidal behavior, 5-10% of whom commit suicide. While multiple initiatives have been developed and implemented for suicide prevention, a key challenge has been the social stigma associated with mental disorders, which deters patients from seeking help or sharing their experiences directly with others including clinicians. This is particularly true for teenagers and younger adults where suicide is the second highest cause of death in the US. Prior research involving surveys and questionnaires (e.g. PHQ-9) for suicide risk prediction failed to provide a quantitative assessment of risk that informed timely clinical decision-making for intervention. Our interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal tendencies and other related mental health conditions afflicting depressed users. We provide details of our learning framework that incorporates domain-specific knowledge to predict the severity of suicide risk for an individual. Our approach involves developing a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions. We also use language modeling, medical entity recognition and normalization and negation detection to create a dataset of 2181 redditors that have discussed or implied suicidal ideation, behavior, or attempt. Given the importance of clinical knowledge, our gold standard dataset of 500 redditors (out of 2181) was developed by four practicing psychiatrists following the guidelines outlined in This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

“…Morales et al [35] showed the strength of CNN and LSTM models for a suicide risk assessment presenting the results for a novelly tested personality and tone features. Bhat et al [36] and [37] highlighted CNN's performance over other approaches to identify the presence of suicidal tendencies among adolescents. Du et al [38] applied deep learning methods to detect psychiatric stressors for suicide recognition in social media.…”

AI enabled suicide prediction tools: a qualitative narrative review

“…Convolutional neural networks was the strongest performer, achieving an overall precision of 70%-40% better than baseline approaches that only applied medical classification systems. 23 A landmark study published in Biomedical Informatics Insights by Coppersmith et al combined many of the insights of previous studies in this area. Coppersmith et al applied natural language processing and supervised and unsupervised machine learning methods to social media data from a variety of sources (eg, Facebook, Twitter, Instagram, Reddit, Tumblr, Strava and Fitbit, among others)-for which they were granted permission by test subjects-in order to determine the risk of attempted suicide.…”

“…Convolutional neural networks was the strongest performer, achieving an overall precision of 70%—40% better than baseline approaches that only applied medical classification systems. 23 …”

Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention

“…The awareness of the severity of suicide has led researchers to assess mental health using social media data for recognizing potential warning signs of suicide in an early stage (Pavalanathan and De Choudhury, 2015;O'dea et al, 2015). In particular, linguistic characteristics (e.g., frequently used words like 'family', 'sad', or 'dream') of social media posts have been extensively investigated (Gaur et al, 2019;Lv et al, 2015). As prior research showed that certain linguistic features revealed in an individual language could be linked to suicide risk (McCarthy, 2010;Sueki, 2015), there have been attempts to develop machine-learning models using a suicide dictionary, which was created and curated by mental health experts.…”

“…As prior research showed that certain linguistic features revealed in an individual language could be linked to suicide risk (McCarthy, 2010;Sueki, 2015), there have been attempts to develop machine-learning models using a suicide dictionary, which was created and curated by mental health experts. For example, an English suicide dictionary was created and validated by four clinical psychiatrists (Gaur et al, 2019); a Chinese suicide dictionary was curated by eleven mental health experts (Lv et al, 2015).…”