Differential Mental Disorder Detection with Psychology-Inspired Multimodal Stimuli

TL;DR

This paper introduces a psychology-inspired multimodal dataset and a novel framework for differential mental disorder detection, leveraging diverse stimuli to improve diagnosis accuracy.

Contribution

It presents a large-scale, clinically verified multimodal mental health dataset and a paradigm-aware framework that captures disorder-specific signals for better diagnosis.

Findings

The framework outperforms existing baselines in disorder detection accuracy.

Psychology-inspired stimuli effectively elicit diverse responses for differential diagnosis.

The dataset covers depression, anxiety, and schizophrenia with clinically verified labels.

Abstract

Differential diagnosis of mental disorders remains a fundamental challenge in real-world clinical practice, where multiple conditions often exhibit overlapping symptoms. However, most existing public datasets are developed under single-disorder settings and rely on limited data elicitation paradigms, restricting their ability to capture disorder-specific patterns. In this work, we investigate differential mental disorder detection through psychology-inspired multimodal stimuli, designed to elicit diverse emotional, cognitive, and behavioral responses grounded in findings from experimental psychology. Based on this paradigm, we collect a large-scale multimodal mental health dataset (MMH) covering depression, anxiety, and schizophrenia, with all diagnostic labels clinically verified by licensed psychiatrists. To effectively model the heterogeneous signals induced by diverse elicitation tasks, we further propose a paradigm-aware multimodal framework that leverages inter-disorder differences prior knowledge as prompt-guided semantic descriptions to capture task-specific affective and interaction contexts for multimodal representation learning in the new differential mental disorder detection task. Extensive experiments show that our framework consistently outperforms existing baselines, underscoring the value of psychology-inspired stimulus design for differential mental disorder detection.