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Predicting imminent suicide risk in a crisis hotline chat using machine learning.

Yossi Levi-Belz1,2, Meytal Grimland3, Yael Segal-Elbak4

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Machine learning accurately predicts imminent suicide risk (IMSR) in real-time crisis chats. Key predictors include specific plans, intent, pain tolerance, self-harm, cognitive rigidity, and impulsivity, aiding suicide prevention efforts.

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HotlineImminent suicide riskMachine learningSuicide ideation

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Area of Science:

  • Psychology
  • Computer Science
  • Public Health

Background:

  • Real-time identification of suicide risk is crucial for prevention.
  • Understanding dynamic mental processes during acute suicide crises is challenging.
  • Existing theories propose predictors for imminent suicide risk (IMSR), but real-time validation is lacking.

Purpose of the Study:

  • To investigate the potential of machine learning for predicting IMSR in internet-based crisis hotline chats.
  • To analyze language patterns associated with psychological factors from suicide crisis theories.
  • To examine the predictive value and temporal stability of these factors during crisis interactions.

Main Methods:

  • Analysis of 3309 anonymized crisis chat sessions (312 identified as IMSR).
  • Compilation of a psychological lexicon based on suicide crisis theories.
  • Extraction of language patterns and application of a logistic regression model for predictor analysis.
  • Temporal analysis to assess predictor stability throughout chat duration.

Main Results:

  • Suicidal ideation with specific plan and intent was the strongest IMSR predictor.
  • Pain tolerance, deliberate self-harm, cognitive rigidity, and impulsivity were significant predictors.
  • Perceived burdensomeness, depressive symptoms, and emotional pain showed negative associations with IMSR.
  • Most identified predictors remained stable throughout the chat sessions.

Conclusions:

  • IMSR is best understood through an integrated approach combining cognitive, affective, and behavioral factors.
  • Identifying indirect risk factors is crucial for real-time suicide risk detection, especially when intentions are not explicit.
  • Machine learning analysis of crisis chats advances theoretical understanding and practical tools for real-time intervention.