Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches.

Journal: International journal of methods in psychiatric research
PMID: 33166430

Abstract

OBJECTIVE: This study explores the prediction of near-term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI), which measures the Suicide Crisis Syndrome, a presuicidal mental state.

Authors

  • Neelang Parghi
    Courant Institute of Mathematical Sciences, New York University, New York City, New York, USA.
  • Lakshmi Chennapragada
    Department of Psychiatry and Behavioral Health, Mount Sinai Beth Israel Medical Center, New York City, New York, USA.
  • Shira Barzilay
    Psychiatry Department, Schneider Children's Medical Centre, Tel Aviv University, Tel Aviv, Israel.
  • Saskia Newkirk
    Department of Psychiatry and Behavioral Health, Mount Sinai Beth Israel Medical Center, New York City, New York, USA.
  • Brian Ahmedani
    Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, USA.
  • Benjamin Lok
    College of Engineering, University of Florida, Gainesville, Florida, USA.
  • Igor Galynker
    Department of Psychiatry and Behavioral Health, Mount Sinai Beth Israel Medical Center, New York City, New York, USA.

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