Deep graph neural network-based prediction of acute suicidal ideation in young adults

Abstract Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predictin...

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Autores principales:	Kyu Sung Choi, Sunghwan Kim, Byung-Hoon Kim, Hong Jin Jeon, Jong-Hoon Kim, Joon Hwan Jang, Bumseok Jeong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/517429c33258464db81d66a2d972bb07
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spelling	oai:doaj.org-article:517429c33258464db81d66a2d972bb072021-12-02T14:53:35ZDeep graph neural network-based prediction of acute suicidal ideation in young adults10.1038/s41598-021-95102-72045-2322https://doaj.org/article/517429c33258464db81d66a2d972bb072021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95102-7https://doaj.org/toc/2045-2322Abstract Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffered from low sensitivity. We developed and validated a deep graph neural network model that increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training; n = 14,238 for testing) using multi-dimensional questionnaires and suicidal ideation within 2 weeks as the prediction target. The best model achieved a sensitivity of 76.3%, specificity of 83.4%, and an area under curve of 0.878 (95% confidence interval, 0.855–0.899). We demonstrated that multi-dimensional deep features covering depression, anxiety, resilience, self-esteem, and clinico-demographic information contribute to the prediction of suicidal ideation. Our model might be useful for the remote evaluation of suicide risk in the general population of young adults for specific situations such as the COVID-19 pandemic.Kyu Sung ChoiSunghwan KimByung-Hoon KimHong Jin JeonJong-Hoon KimJoon Hwan JangBumseok JeongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kyu Sung Choi Sunghwan Kim Byung-Hoon Kim Hong Jin Jeon Jong-Hoon Kim Joon Hwan Jang Bumseok Jeong Deep graph neural network-based prediction of acute suicidal ideation in young adults
description	Abstract Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffered from low sensitivity. We developed and validated a deep graph neural network model that increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training; n = 14,238 for testing) using multi-dimensional questionnaires and suicidal ideation within 2 weeks as the prediction target. The best model achieved a sensitivity of 76.3%, specificity of 83.4%, and an area under curve of 0.878 (95% confidence interval, 0.855–0.899). We demonstrated that multi-dimensional deep features covering depression, anxiety, resilience, self-esteem, and clinico-demographic information contribute to the prediction of suicidal ideation. Our model might be useful for the remote evaluation of suicide risk in the general population of young adults for specific situations such as the COVID-19 pandemic.
format	article
author	Kyu Sung Choi Sunghwan Kim Byung-Hoon Kim Hong Jin Jeon Jong-Hoon Kim Joon Hwan Jang Bumseok Jeong
author_facet	Kyu Sung Choi Sunghwan Kim Byung-Hoon Kim Hong Jin Jeon Jong-Hoon Kim Joon Hwan Jang Bumseok Jeong
author_sort	Kyu Sung Choi
title	Deep graph neural network-based prediction of acute suicidal ideation in young adults
title_short	Deep graph neural network-based prediction of acute suicidal ideation in young adults
title_full	Deep graph neural network-based prediction of acute suicidal ideation in young adults
title_fullStr	Deep graph neural network-based prediction of acute suicidal ideation in young adults
title_full_unstemmed	Deep graph neural network-based prediction of acute suicidal ideation in young adults
title_sort	deep graph neural network-based prediction of acute suicidal ideation in young adults
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/517429c33258464db81d66a2d972bb07
work_keys_str_mv	AT kyusungchoi deepgraphneuralnetworkbasedpredictionofacutesuicidalideationinyoungadults AT sunghwankim deepgraphneuralnetworkbasedpredictionofacutesuicidalideationinyoungadults AT byunghoonkim deepgraphneuralnetworkbasedpredictionofacutesuicidalideationinyoungadults AT hongjinjeon deepgraphneuralnetworkbasedpredictionofacutesuicidalideationinyoungadults AT jonghoonkim deepgraphneuralnetworkbasedpredictionofacutesuicidalideationinyoungadults AT joonhwanjang deepgraphneuralnetworkbasedpredictionofacutesuicidalideationinyoungadults AT bumseokjeong deepgraphneuralnetworkbasedpredictionofacutesuicidalideationinyoungadults
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Deep graph neural network-based prediction of acute suicidal ideation in young adults

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