Build Machine Learning Models to Predict Suicidal Thoughts and Examine the Feasibility of Using Just-in-time Adaptive Interventions for Suicide Prevention

We aim to construct machine learning models to predict suicidal thoughts based on linguistic, cognitive, emotional, and behavioral factors and examine the feasibility of using Just-In-Time Adaptive Interventions (JITAIs) for suicide prevention. Study 1 will build machine learning models based on ecological momentary assessments (EMAs) data from individuals with and without suicidal ideation risk. Study 2 will convene focus groups of health professionals to determine how to tailor interventions to individuals at different suicide risk levels. Study 3 will conduct in-depth interviews with at-risk individuals to understand their opinions on using JITAIs for suicide prevention.