Citation

Colic S, Richardson DJ, James Reilly P, Gary Hasey M. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2018; 2018: 4936-4939.

Copyright

(Copyright © 2018, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC.2018.8513200

PMID

30441450

Abstract

Combat veterans; especially those with mental health conditions are an at risk group for suicidal ideation and behaviour. This study attempts to use machine learning algorithm to predict suicidal ideation (SI) in a treatment seeking veteran population. Questionnaire data from 738 patients consisting of veterans, still serving members of the Canadian Forces (CF) and Royal Canadian Mountain Police (RCMP) were examined to determine the likelihood of suicide ideation and to identify key variables for tracking the risk of suicide. Unlike conventional approaches we use pattern recognition methods, known collectively as machine learning (ML), to examine multivariate data and identify patterns associate with suicidal ideation. Our findings show that accurate prediction of SI of over 84.4% can be obtained with 25 variables, and 81% using as little as 10 variables primarily obtained from the patient health questionnaire (PHQ). Surprisingly the best identifiers for SI did not come from occupational experiences but rather the patient quality of health, signifying that these findings could be applied to the general population. Our results suggest that ML could assist clinicians to develop a better screening aid for suicidal ideation and behaviour.

Language: en