@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Dash-M5H: An Interactive Dashboard for Multi-Modal, Multi-Model Mental Health Assessment
%A Alavo, Raymond
%A Zhang, Xinyuan
%A Ademaj, Gemza
%A Cai, Junhui
%A Kwon, Hyeokhyen
%A Cotes, Robert
%A Clifford, Gari D.
%A Abbasi, Ahmed
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F alavo-etal-2026-dash
%X 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.
%U https://aclanthology.org/2026.acl-demo.14/
%P 138-147
Markdown (Informal)
[Dash-M5H: An Interactive Dashboard for Multi-Modal, Multi-Model Mental Health Assessment](https://aclanthology.org/2026.acl-demo.14/) (Alavo et al., ACL 2026)
ACL
- Raymond Alavo, Xinyuan Zhang, Gemza Ademaj, Junhui Cai, Hyeokhyen Kwon, Robert Cotes, Gari D. Clifford, and Ahmed Abbasi. 2026. Dash-M5H: An Interactive Dashboard for Multi-Modal, Multi-Model Mental Health Assessment. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 138–147, San Diego, California, United States. Association for Computational Linguistics.