0 papers in the Multimodal Machine Learning research area.
Discover 1 peer-reviewed study in Multimodal Machine Learning (2025). Explore research findings powered by Prolific's diverse participant panel.
This page lists 1 peer-reviewed paper in the research area of Multimodal Machine Learning in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
Authors: B Grimm, P Yilmam, B Talbot, L Larsen
Year: 2025
Published in: npj Digital Medicine, 2025 - nature.com
Institution: Videra Health
Research Area: Computational Mental Health Assessment, Multimodal Machine Learning
Discipline: Computational Health, Digital Medicine
A multimodal machine learning model using text (MPNet) and voice (HuBERT) analysis predicts depression, anxiety, and trauma from a single video-based question with strong performance and demographic consistency while significantly reducing assessment time.
Methods: Multimodal analysis combining MPNet for textual data and HuBERT for prosodic voice features trained on video-based responses.
Key Findings: Efficient prediction of self-reported scores for depression (PHQ-9), anxiety (GAD-7), and trauma (PCL-5) from brief video responses.
Sample Size: 2420
Related Disciplines: Computational Health, Digital Medicine
Related Institutions: Videra Health
Researchers: B Grimm, P Yilmam, B Talbot, L Larsen
Publication Years: 2025
Browse all papers in the Prolific Citations Library
