Adaptive Language-based Mental Health Assessment with Item-Response Theory

Abstract

Mental health issues vary widely across individuals, manifesting in heterogeneous signs and symptoms. This study introduces an adaptive language-based assessment framework using Item Response Theory (IRT) to optimize question selection and reduce response burdens without compromising diagnostic accuracy. The proposed ALIRT method achieved over 90% variance explained with only three questions for depression assessments.