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Identification of Suicidal Ideation in the Canadian Community Health Survey—Mental Health Component Using Deep Learning / Sneha Desai, Myriam Tanguay-Sela, David Benrimoh, Robert Fratila, Eleanor Brown, Kelly Perlman, Ann John, Marcos del Pozo Banos, Nancy Low, Sonia Israel, Lisa Palladini, Gustavo Turecki

Frontiers in Artificial Intelligence, Volume: 4

Swansea University Authors: Ann John, Marcos del Pozo Banos

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    © 2021 Desai, Tanguay-Sela, Benrimoh, Fratila, Brown, Perlman, John, DelPozo-Banos, Low, Israel, Palladini and Turecki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).

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Abstract

Introduction: Suicidal ideation (SI) is prevalent in the general population, and is a risk factor for suicide. Predicting which patients are likely to have SI remains challenging. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, a...

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Published in: Frontiers in Artificial Intelligence
ISSN: 2624-8212
Published: Frontiers Media SA 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa56968
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Abstract: Introduction: Suicidal ideation (SI) is prevalent in the general population, and is a risk factor for suicide. Predicting which patients are likely to have SI remains challenging. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete datasets. An automated screening system for SI could help prompt clinicians to be moreattentive to patients at risk for suicide.Methods: Using the Canadian Community Health Survey - Mental Health Component, we trained a DL model based on 23,859 survey responses to classify patients with and without SI. Models were created to classify both lifetime and last 12 month SI. From 582 possible parameters we produced 96 and 21 feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI; validation was done on held-out data.Results: For lifetime SI, the 96 feature model had an AUC of 0.79 and the 21 feature model had an AUC of 0.77. For SI in the last 12 months the 96 feature model had an AUC of 0.71 and the 21 feature model had an AUC of 0.68. In addition, sensitivity analyses demonstrated feature relationships in line with existing literature. Discussion: Although requiring further study to ensure clinical relevance and sample generalizability, this study is an initial proof of concept for the use of DL to improve identification of SI. Sensitivity analyses can help improve the interpretability of DL models. This kind of model would help start conversations with patients which could lead to improved care and a reduction in suicidal behavior.
Keywords: deep learning, suicidal ideation, risk assessment, predictors, machine learning, artificial intelligence,Canadian community health survey, Canadian community health survey—mental health 2012
College: Swansea University Medical School