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Predictive Machine Learning Models for Suicidal Ideation Trajectory Detection

Affective Science

Adolescent Mood Disorders & Digital Phenotyping

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Affective Science › Adolescent Mood Disorders & Digital Phenotyping

Predictive Machine Learning Models for Suicidal Ideation Trajectory Detection

Investigation employs longitudinal machine learning approaches trained on multimodal digital phenotypes to prospectively identify adolescents at elevated risk for suicidal ideation escalation. This research produces clinically actionable predictive instruments that operationalize real-time digital monitoring for suicide prevention.

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📚 Academic: Thesis & PPT assistance included🧪 Tech: Master the protocols hands-on📝 Research > 3 months: Publication co-authorship in a Scopus-indexed journal

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