ArticleNPJ digital medicine2025
Multimodal machine learning for video based single question mental health assessment.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study demonstrates that a single video question can predict self-reported depression (PHQ-9), anxiety (GAD-7), and trauma (PCL-5) through text and voice analysis. As mental health screening needs increase, efficient multi-condition assessment methods could reduce patient burden in clinical settings. Our multimodal model, integrating MPNet for textual analysis and HuBERT for voice prosody, was trained on data from 2420 participants. Our approach achieves 64.6% reduced assessment time (78.4 s vs 221.7 s) while screening all three conditions from one response, with only 1.4% of participants unwilling to use video-based screening. Results demonstrate strong performance and demographic consistency across age, gender, and race/ethnicity supporting the feasibility of efficient multi-condition screening from brief video responses.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.