Predicting suicide risk in real-time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box

Meytal Grimland, Joy Benatov, Hadas Yeshayahu, Daniel Izmaylov, Avi Segal, Kobi Gal, Yossi Levi-Belz

Research output: Contribution to journalArticlepeer-review


Background: This study addresses the suicide risk predicting challenge by exploring the predictive ability of machine learning (ML) models integrated with theory-driven psychological risk factors in real-time crisis hotline chats. More importantly, we aimed to understand the specific theory-driven factors contributing to the ML prediction of suicide risk. Method: The dataset consisted of 17,654 crisis hotline chat sessions classified dichotomously as suicidal or not. We created a suicide risk factors-based lexicon (SRF), which encompasses language representations of key risk factors derived from the main suicide theories. The ML model (Suicide Risk-Bert; SR-BERT) was trained using natural language processing techniques incorporating the SRF lexicon. Results: The results showed that SR-BERT outperformed the other models. Logistic regression analysis identified several theory-driven risk factors significantly associated with suicide risk, the prominent ones were hopelessness, history of suicide, self-harm, and thwarted belongingness. Limitations: The lexicon is limited in its ability to fully encompass all theoretical concepts related to suicide risk, nor to all the language expressions of each concept. The classification of chats was determined by trained but non-professionals in metal health. Conclusion: This study highlights the potential of how ML models combined with theory-driven knowledge can improve suicide risk prediction. Our study underscores the importance of hopelessness and thwarted belongingness in suicide risk and thus their role in suicide prevention and intervention.

Original languageEnglish
JournalSuicide and Life-Threatening Behavior
Early online date12 Feb 2024
StatePublished Online - 12 Feb 2024


  • crisis chat hotlines
  • hopelessness
  • machine learning
  • natural language processing
  • suicide

All Science Journal Classification (ASJC) codes

  • Clinical Psychology
  • Public Health, Environmental and Occupational Health
  • Psychiatry and Mental health


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