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Prediction of Repeated Self-Harm in Six Months: Comparison of Traditional Psychometrics With Random Forest Algorithm.

Suicidal risk has been a significant mental health problem. However, the predictive ability for repeated self-harm (SH) has not improved over the past decades. This study thus aimed to explore a potential tool with theoretical accommodation and clinical application by employing traditional logistic...

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Publicado en:Omega: Journal of Death & Dying Vol. 88; no. 4; pp. 1403 - 1430
Autores principales: Chen, Shu-Chin, Huang, Hui-Chun, Liu, Shen-Ing, Chen, Sue-Huei
Formato: Artículo
Publicado: Sage Publications Inc. Mar2024
Materias:
Suicide risk factors
Psychometrics
Self-mutilation
Confidence intervals
Random forest algorithms
Machine learning
Risk assessment
Comparative studies
Descriptive statistics
Prediction models
Logistic regression analysis
Algorithms
Acceso en línea:Ver este registro en EBSCOhost