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

Journal: Suicide & life-threatening behavior
PMID: 38345174

Abstract

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.

Authors

  • Meytal Grimland
    Lior Tsfaty Center for Suicide and Mental Pain Studies, Ruppin Academic Center, Emek Hefer, Israel.
  • Joy Benatov
    Department of Special Education, University of Haifa, Haifa, Israel.
  • Hadas Yeshayahu
    Department of Special Education, University of Haifa, Haifa, Israel.
  • Daniel Izmaylov
    Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
  • Avi Segal
    Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
  • Kobi Gal
    Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
  • Yossi Levi-Belz
    Lior Tsfaty Center for Suicide and Mental Pain Studies, Ruppin Academic Center, Emek Hefer, Israel.

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