[Paper Review] Comparing Suicide Risk Insights derived from Clinical and Social Media data

Rohith Kumar Thiruvalluru, Manas Gaur+3 more|arXiv (Cornell University)|Dec 18, 2020

Suicide and Self-Harm Studies12 references4 citations

TL;DR

This study compares suicide risk factors (SRFs) in electronic health records (EHRs) from 123,703 patients and social media posts from r/SuicideWatch (n=2,180 users, ~30,000 posts), using semantic embeddings to cluster and analyze unstructured text. It reveals significant disparities: EHRs emphasize depressive feelings (24.3%), psychological disorders (21.1%), and drug abuse (18.2%), while r/SuicideWatch highlights gun ownership (17.3%), self-harm (14.6%), and bullying (13.2%); critical SRFs like family violence and racial discrimination are underrepresented in both, underscoring the need for integrated clinical-social media data for comprehensive suicide risk assessment.

ABSTRACT

Suicide is the 10th leading cause of death in the US and the 2nd leading cause of death among teenagers. Clinical and psychosocial factors contribute to suicide risk (SRFs), although documentation and self-expression of such factors in EHRs and social networks vary. This study investigates the degree of variance across EHRs and social networks. We performed subjective analysis of SRFs, such as self-harm, bullying, impulsivity, family violence/discord, using >13.8 Million clinical notes on 123,703 patients with mental health conditions. We clustered clinical notes using semantic embeddings under a set of SRFs. Likewise, we clustered 2180 suicidal users on r/SuicideWatch (~30,000 posts) and performed comparative analysis. Top-3 SRFs documented in EHRs were depressive feelings (24.3%), psychological disorders (21.1%), drug abuse (18.2%). In r/SuicideWatch, gun-ownership (17.3%), self-harm (14.6%), bullying (13.2%) were Top-3 SRFs. Mentions of Family violence, racial discrimination, and other important SRFs contributing to suicide risk were missing from both platforms.

Motivation & Objective

• To investigate the similarities and differences in suicide risk factor (SRF) expression between clinical EHRs and social media platforms.
• To assess the feasibility of using semantic embeddings to cluster and analyze unstructured SRFs in both EHR and social media text.
• To identify underrepresented or missing SRFs—such as family violence, racial discrimination, and socioeconomic stressors—across both data sources.
• To demonstrate the complementary value of combining EHR and social media data for early suicide risk detection and intervention.
• To create a publicly available, annotated dataset and open-source code for reproducibility and future research.

Proposed method

• Constructed a domain-specific SRF lexicon combining PHQ-9, C-SSRS, DAO, and SNOMED-CT concepts for semantic alignment.
• Applied semantic embeddings to cluster clinical notes from Weill Cornell Medicine EHRs and r/SuicideWatch posts based on SRFs.
• Performed subjective analysis on clustered SRFs to identify top-occurring factors and novel categories like 'Other Important SRFs' and 'Accessory'.
• Collected and analyzed user posts across r/SuicideWatch and related MH-subreddits to capture broader mental health narratives.
• Classified SRFs into distinct categories: Top-3 SRFs, 'Other Important SRFs' (e.g., relationship issues, unemployment), and 'Accessory' (e.g., tools used in suicide attempts).
• Used a hybrid approach combining clinical knowledge with NLP techniques to ensure semantic and clinical relevance in SRF detection.

Experimental results

Research questions

• RQ1 How do the top suicide risk factors (SRFs) expressed in electronic health records (EHRs) compare to those in social media posts from r/SuicideWatch?
• RQ2 To what extent are clinically significant SRFs—such as family violence, racial discrimination, and physiological stressors—represented in both EHR and social media data?
• RQ3 What novel or underrepresented SRFs emerge from social media that are absent or underrepresented in EHRs?
• RQ4 How do semantic clustering techniques enable the identification and comparison of SRFs across unstructured EHR and social media text?
• RQ5 What are the limitations of current SRF lexicons and data sources in capturing the full spectrum of suicide risk factors?

Key findings

• In EHRs, the top three SRFs were depressive feelings (24.3%), psychological disorders (21.1%), and drug abuse (18.2%), reflecting clinical documentation patterns.
• On r/SuicideWatch, the top three SRFs were gun ownership (17.3%), self-harm (14.6%), and bullying (13.2%), indicating distinct social media expression of risk.
• Critical SRFs such as family violence, racial discrimination, and physiological stressors were notably missing or underrepresented in both EHR and social media datasets.
• The study identified 'Other Important SRFs' including relationship issues, financial distress, and unemployment, which were not mapped to existing SRF lists and require clinical labeling.
• The 'Accessory' category—referring to tools or methods used in suicide attempts—was also heterogeneous and semantically incohesive, limiting comparative analysis.
• The study developed and will release a publicly available, post-level and user-level annotated dataset from r/SuicideWatch, along with open-source code for reproducibility.