Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features.

The objective of the current study was to build predictive models for suicidal ideation in a sample of children aged 9-10 using features previously implicated in risk among older adolescent and adult populations. This case-control analysis utilized baseline data from the Adolescent Brain and Cogniti...

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Autores principales:	Gareth Harman, Dakota Kliamovich, Angelica M Morales, Sydney Gilbert, Deanna M Barch, Michael A Mooney, Sarah W Feldstein Ewing, Damien A Fair, Bonnie J Nagel
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f6f4c69e41d74c38a13d5117c1a902de
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spelling	oai:doaj.org-article:f6f4c69e41d74c38a13d5117c1a902de2021-12-02T20:05:31ZPrediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features.1932-620310.1371/journal.pone.0252114https://doaj.org/article/f6f4c69e41d74c38a13d5117c1a902de2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252114https://doaj.org/toc/1932-6203The objective of the current study was to build predictive models for suicidal ideation in a sample of children aged 9-10 using features previously implicated in risk among older adolescent and adult populations. This case-control analysis utilized baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study, collected from 21 research sites across the United States (N = 11,369). Several regression and ensemble learning models were compared on their ability to classify individuals with suicidal ideation and/or attempt from healthy controls, as assessed by the Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version. When comparing control participants (mean age: 9.92±0.62 years; 4944 girls [49%]) to participants with suicidal ideation (mean age: 9.89±0.63 years; 451 girls [40%]), both logistic regression with feature selection and elastic net without feature selection predicted suicidal ideation with an AUC of 0.70 (CI 95%: 0.70-0.71). The random forest with feature selection trained to predict suicidal ideation predicted a holdout set of children with a history of suicidal ideation and attempt (mean age: 9.96±0.62 years; 79 girls [41%]) from controls with an AUC of 0.77 (CI 95%: 0.76-0.77). Important features from these models included feelings of loneliness and worthlessness, impulsivity, prodromal psychosis symptoms, and behavioral problems. This investigation provided an unprecedented opportunity to identify suicide risk in youth. The use of machine learning to examine a large number of predictors spanning a variety of domains provides novel insight into transdiagnostic factors important for risk classification.Gareth HarmanDakota KliamovichAngelica M MoralesSydney GilbertDeanna M BarchMichael A MooneySarah W Feldstein EwingDamien A FairBonnie J NagelPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0252114 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gareth Harman Dakota Kliamovich Angelica M Morales Sydney Gilbert Deanna M Barch Michael A Mooney Sarah W Feldstein Ewing Damien A Fair Bonnie J Nagel Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features.
description	The objective of the current study was to build predictive models for suicidal ideation in a sample of children aged 9-10 using features previously implicated in risk among older adolescent and adult populations. This case-control analysis utilized baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study, collected from 21 research sites across the United States (N = 11,369). Several regression and ensemble learning models were compared on their ability to classify individuals with suicidal ideation and/or attempt from healthy controls, as assessed by the Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version. When comparing control participants (mean age: 9.92±0.62 years; 4944 girls [49%]) to participants with suicidal ideation (mean age: 9.89±0.63 years; 451 girls [40%]), both logistic regression with feature selection and elastic net without feature selection predicted suicidal ideation with an AUC of 0.70 (CI 95%: 0.70-0.71). The random forest with feature selection trained to predict suicidal ideation predicted a holdout set of children with a history of suicidal ideation and attempt (mean age: 9.96±0.62 years; 79 girls [41%]) from controls with an AUC of 0.77 (CI 95%: 0.76-0.77). Important features from these models included feelings of loneliness and worthlessness, impulsivity, prodromal psychosis symptoms, and behavioral problems. This investigation provided an unprecedented opportunity to identify suicide risk in youth. The use of machine learning to examine a large number of predictors spanning a variety of domains provides novel insight into transdiagnostic factors important for risk classification.
format	article
author	Gareth Harman Dakota Kliamovich Angelica M Morales Sydney Gilbert Deanna M Barch Michael A Mooney Sarah W Feldstein Ewing Damien A Fair Bonnie J Nagel
author_facet	Gareth Harman Dakota Kliamovich Angelica M Morales Sydney Gilbert Deanna M Barch Michael A Mooney Sarah W Feldstein Ewing Damien A Fair Bonnie J Nagel
author_sort	Gareth Harman
title	Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features.
title_short	Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features.
title_full	Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features.
title_fullStr	Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features.
title_full_unstemmed	Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features.
title_sort	prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f6f4c69e41d74c38a13d5117c1a902de
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Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features.

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