Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data.

Journal: PLoS medicine
PMID: 33156863

Abstract

BACKGROUND: Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for prediction of suicidal behavior.

Authors

  • Qi Chen
    Department of Gastroenterology, Jining First People's Hospital, Jining, China.
  • Yanli Zhang-James
    Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA.
  • Eric J Barnett
    Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, New York, United States of America.
  • Paul Lichtenstein
    Department of Medical Epidemiology and Biostatics, Karolinska Institutet, Stockholm, Sweden.
  • Jussi Jokinen
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
  • Brian M D'Onofrio
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Stephen V Faraone
    Department of Psychiatry, State University of New York Upstate Medical University, Syracuse.
  • Henrik Larsson
    Department of Medical Epidemiology and Biostatics, Karolinska Institutet, Stockholm, Sweden.
  • Seena Fazel
    Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom.

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