@inproceedings{alavo-etal-2026-dash,
    title = "Dash-{M}5{H}: An Interactive Dashboard for Multi-Modal, Multi-Model Mental Health Assessment",
    author = "Alavo, Raymond  and
      Zhang, Xinyuan  and
      Ademaj, Gemza  and
      Cai, Junhui  and
      Kwon, Hyeokhyen  and
      Cotes, Robert  and
      Clifford, Gari D.  and
      Abbasi, Ahmed",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.14/",
    pages = "138--147",
    ISBN = "979-8-89176-392-0",
    abstract = "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."
}