Leveraging Acoustic-Linguistic Analytics and Social Determinants to Enhance Suicide Prevention Efforts in Veterans Crisis Line Interventions
Description
This project leverages machine learning (ML) and data analytics to enhance suicide prevention efforts by improving risk prediction, crisis intervention effectiveness, and understanding of social and environmental factors influencing Veteran suicide risk. It will develop a data processing pipeline to extract and analyze VCL call data directly from call audio recordings, enabling multimodal ML models that integrate linguistic, acoustic, and contextual features to identify imminent suicide risk. Additionally, it will evaluate crisis intervention effectiveness and link VCL call data with external datasets to assess environmental stressors such as noise pollution. Finally, models will be validated on a larger dataset within the Veterans Affairs (VA) Cloud to ensure scalability and internally-sustainable integration into VCL workflows, delivering artificial intelligence (AI)-driven decision-support tools and actionable policy insights to improve crisis response strategies.
Detailed example
VCL call data analysis to identify imminent suicide risk and evaluate crisis intervention effectiveness.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Improve VCL protocols and procedures; improve suicide prevention efforts; increase efficiency; enhance patient outcomes.
Controls / human review
ATO: Not reported; PIA: Not published