Machine learning-based prediction for self-harm and suicide attempts in adolescents.
Psychiatry Res
; 328: 115446, 2023 10.
Article
em En
| MEDLINE
| ID: mdl-37683319
ABSTRACT
This study aimed to use machine learning (ML) models to predict the risk of self-harm and suicide attempts in adolescents. We conducted secondary analysis of cross-sectional data from the Longitudinal Study of Australian Children dataset. Several key variables at the age of 14-15 years were used to predict self-harm or suicide attempt at 16-17 years. Random forest classification models were used to select the optimal subset of predictors and subsequently make predictions. Among 2809 participants, 296 (10.54%) reported an act of self-harm and 145 (5.16%) reported attempting suicide at least once in the past 12 months. The area under the receiver operating curve was fair for self-harm (0.7397) and suicide attempt (0.7220), which outperformed the prediction strategy solely based on prior suicide or self-harm attempt (AUC 0.6). The most important factors identified were similar, and included depressed feelings, strengths and difficulties questionnaire scores, perceptions of self, and school- and parent-related factors. The random forest classification algorithm, an ML technique, can effectively select the optimal subset of predictors from hundreds of variables to forecast the risks of suicide and self-harm among adolescents. Further research is needed to validate the utility and scalability of ML techniques in mental health research.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tentativa de Suicídio
/
Comportamento Autodestrutivo
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Observational_studies
/
Prevalence_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adolescent
/
Child
/
Humans
País/Região como assunto:
Oceania
Idioma:
En
Revista:
Psychiatry Res
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Austrália