Leveraging Artificial Intelligence and Machine Learning to Reduce Disparities in Suicide Trajectories: A BioPsychosocial Approach to Identify Modifiable Risk and Protective Processes

Funding period:
February 1, 2021 - February 1, 2022

This interdisciplinary project will use state-of-art machine learning techniques to identify modifiable risk and protective processes in suicidal trajectories from a biopsychosocial perspective. By leveraging artificial intelligence in unique multimodal data across the biomedical image, social networks, clinical, psychological, and behavioral domains collected over time, this study will ultimately produce an easy-to-interpret, culturally appropriate, and accessible tool to clinicians, social workers, community health practitioners, and policymakers in suicide prevention programs.

With close collaboration between social work and AI experts, this project will make fundamental scientific contributions to computer or information sciences, social, behavioral, cognitive, and economics. The team will address the alarming disparities in suicide across developmental trajectories from children, adolescents to young adults. The integrated algorithm will allow clinicians, families, teachers, and community health practitioners to identify young people at risk before them being in crisis so that clinicians can intervene earlier, which could have a significant contribution.

The findings of this project will advance understanding of how AI-learning computation, combined with advances in social, psychological, and biomedical data analysis. Clinically, it will improve our knowledge of the origins, modifiable risks, and protective processes of disparities in suicide trajectories.