  We employ a Large Language Model (LLM) to convert unstructured psychological
interviews into structured questionnaires spanning various psychiatric and
personality domains. The LLM is prompted to answer these questionnaires by
impersonating the interviewee. The obtained answers are coded as features,
which are used to predict standardized psychiatric measures of depression
(PHQ-8) and PTSD (PCL-C), using a Random Forest regressor. Our approach is
shown to enhance diagnostic accuracy compared to multiple baselines. It thus
establishes a novel framework for interpreting unstructured psychological
interviews, bridging the gap between narrative-driven and data-driven
approaches for mental health assessment.
