Dash-M5H: An Interactive Dashboard for Multi-Modal, Multi-Model Mental Health Assessment
Raymond Alavo University of Notre Dame, Xinyuan Zhang University of Notre Dame, Gemza Ademaj, Junhui Cai University of Notre Dame, Hyeokhyen Kwon Emory University, Robert Cotes Emory University, Gari D. Clifford Emory University and Georgia Institute of Technology, Ahmed Abbasi University of Notre Dame
We present Dash-M5H, an interactive dashboard for multi-modal, multi-model mental health assessment that helps clinicians and researchers jointly inspect multimodal behavioral data with multi-model signal outputs of recorded clinical interviews. Guided by signal detection and integrated sensemaking theories, Dash-M5H synchronizes transcript text, audio, and facial behavior (action units and gaze) to support overview-to-detail evidence tracing; and it integrates extracted signals (e.g., sentiment and facial activity) with a clinically grounded VLM prediction pipeline that produces DSM-5-aligned depression predictions. Dash-M5H (https://dash-m5h.io) is implemented in a lightweight, browser-based stack (Quarto + Observable JS + D3), supports local data import and time-synced clinical annotation with export. We demonstrate Dash-M5H through a depression screening scenario, evaluate its note-taking and screening capabilities through a user experiment, and release a live demo (https://youtu.be/w3qCJ02k6bw) and code (https://github.com/nd-hal/M5H-Dashboard-VLM) to facilitate reproducible evaluation.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表