Suicide is the leading cause of death among youths in Australia. The urgent need for effective prevention and intervention strategies cannot be overstated. Unfortunately, current approaches to reducing suicide risk for adolescents often rely on subjective assessments by clinicians and the personal preference of patients and/or families. As a result, these strategies lack an evidence-based rationale and may not lead to reliable and satisfactory outcomes. Additionally, clinical heterogeneity poses significant challenges in designing effective suicide prevention and intervention strategies.
To address these challenges, we propose to explore the joint effect of multiple factors associated with heterogeneous treatment outcomes to optimise the individualised treatment plan using an ensemble of machine learning techniques. We believe that the findings will lead to innovative strategies that lead to a greater suicide risk reduction for youths.
How to Apply
Express your interest in this project by emailing Dr Daniel Lin at daniel.lin@unsw.edu.au. Include a copy of your CV and your academic transcript(s).
School / Research Area
Clinical Medicine
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