Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

Journal: General hospital psychiatry
Published Date: Jul 1, 2017

Abstract

OBJECTIVE: Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide.

Authors

  • Nuwan C Hettige
    Group for Suicide Studies, Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario M5T 1R8, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.
  • Thai Binh Nguyen
    Group for Suicide Studies, Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario M5T 1R8, Canada.
  • Chen Yuan
    Group for Suicide Studies, Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario M5T 1R8, Canada.
  • Thanara Rajakulendran
    Group for Suicide Studies, Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario M5T 1R8, Canada.
  • Jermeen Baddour
    Group for Suicide Studies, Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario M5T 1R8, Canada.
  • Nikhil Bhagwat
    Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
  • Ali Bani-Fatemi
    Group for Suicide Studies, Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario M5T 1R8, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.
  • Aristotle N Voineskos
    Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Geriatric Mental Health Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.
  • M Mallar Chakravarty
    Douglas Mental Health University Institute, McGill University, Montreal, Canada; Department of Psychiatry, McGill University, Montreal, Canada; Biological and Biomedical Engineering, McGill University, Montreal, Canada.
  • Vincenzo De Luca
    Group for Suicide Studies, Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario M5T 1R8, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. Electronic address: vincenzo_deluca@camh.net.

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