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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: Harman, Gareth, Kliamovich, Dakota, Morales, Angelica M., Gilbert, Sydney, Barch, Deanna M., Mooney, Michael A., Feldstein Ewing, Sarah W., Fair, Damien A., Nagel, Bonnie J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8148349/
https://www.ncbi.nlm.nih.gov/pubmed/34033672
http://dx.doi.org/10.1371/journal.pone.0252114
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author Harman, Gareth
Kliamovich, Dakota
Morales, Angelica M.
Gilbert, Sydney
Barch, Deanna M.
Mooney, Michael A.
Feldstein Ewing, Sarah W.
Fair, Damien A.
Nagel, Bonnie J.
author_facet Harman, Gareth
Kliamovich, Dakota
Morales, Angelica M.
Gilbert, Sydney
Barch, Deanna M.
Mooney, Michael A.
Feldstein Ewing, Sarah W.
Fair, Damien A.
Nagel, Bonnie J.
author_sort Harman, Gareth
collection PubMed
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.
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spelling pubmed-81483492021-06-07 Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features Harman, Gareth Kliamovich, Dakota Morales, Angelica M. Gilbert, Sydney Barch, Deanna M. Mooney, Michael A. Feldstein Ewing, Sarah W. Fair, Damien A. Nagel, Bonnie J. PLoS One Research Article 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. Public Library of Science 2021-05-25 /pmc/articles/PMC8148349/ /pubmed/34033672 http://dx.doi.org/10.1371/journal.pone.0252114 Text en © 2021 Harman et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Harman, Gareth
Kliamovich, Dakota
Morales, Angelica M.
Gilbert, Sydney
Barch, Deanna M.
Mooney, Michael A.
Feldstein Ewing, Sarah W.
Fair, Damien A.
Nagel, Bonnie J.
Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8148349/
https://www.ncbi.nlm.nih.gov/pubmed/34033672
http://dx.doi.org/10.1371/journal.pone.0252114
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