Predicting death by suicide using administrative health care system data: Can feedforward neural network models improve upon logistic regression models?

Journal: Journal of affective disorders
Published Date: Jul 30, 2019

Abstract

BACKGROUND: Suicide is a leading cause of death worldwide. With the increasing volume of administrative health care data, there is an opportunity to evaluate whether machine learning models can improve upon statistical models for quantifying suicide risk.

Authors

  • Michael Sanderson
    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, TRW, 4th Floor, Room 4D66, 3280 Hospital Drive NW, Calgary, Alberta, Canada. Electronic address: michael.sanderson@gov.ab.ca.
  • Andrew G M Bulloch
    Hotchkiss Brain Institute, Department of Psychiatry, Cumming School of Medicine, University of Calgary, Canada.
  • JianLi Wang
    School of Epidemiology, Public Health and Preventive Medicine, Department of Psychiatry, Faculty of Medicine, University of Ottawa, Royal Ottawa Mental Health Centre, 1145 Carling Avenue, Ottawa, Ontario, Canada.
  • Tyler Williamson
    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, TRW, 3rd Floor, Room 3D15, 3280 Hospital Drive NW, Calgary, Alberta, Canada.
  • Scott B Patten
    Department of Community Health Sciences, Department of Psychiatry, Cumming School of Medicine, University of Calgary, TRW, 4th Floor, Room 4D66, 3280 Hospital Drive NW, Calgary, Alberta, Canada.

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