A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data

CONCLUSIONS: The distinct factors associated with SI and SA across diagnostic subgroups highlight the importance of targeted, subgroup-specific suicide prevention interventions. These findings emphasize the value of precise, data-driven approaches for suicide prevention among diverse populations and individuals with disabilities across the life-course.

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