ArticleNpj mental health research2024
Harnessing multimodal approaches for depression detection using large language models and facial expressions.
Article in Npj mental health research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multimodal observable cues in mood, anxiety, and borderline personality disorders: a review of reviews to inform explainable AI in mental health.Frontiers in artificial intelligence · 2025Pooled it
- Digital mental health: innovations in assessment and intervention.Journal of Zhejiang University. Science. B · 2026Article
- Three-dimensional face reconstruction for emotional dynamics: a novel approach to distinguishing bipolar disorder from major depressive disorder.Journal of Zhejiang University. Science. B · 2026Article
- Leakage-Free Multimodal Depression Screening: Controlled Evaluation of Text, Facial Behavior, and Prosodic Fusion.Bioengineering (Basel, Switzerland) · 2026Article
- Large Language Models for Depression Detection: A Review with Prospects of Incomplete Multimodality.Brain sciences · 2026Review
- Large language model empowered explainable and interpretable mental health analysis.Scientific reports · 2026Article
- Machine learning and natural language processing for the identification of potential mental disorders among school-age children: a prospective birth cohort study.BMC medicine · 2026Article
- Explainable Text-Based Depression and Suicide Risk Prediction from Social Media Using Deep Learning and Graph Neural Networks.Healthcare (Basel, Switzerland) · 2026Article
- Large Language Models and Their Applications in Mental Health: Scoping Review.JMIR mental health · 2026Article
- Computational Analysis of Expressive Behavior in Clinical Assessment.Annual review of clinical psychology · 2026Review
- Language-based detection of depression with machine learning: systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Reimagining psychiatric care with agentic AI: promise, challenges, and a roadmap forward.NPJ digital medicine · 2026Review
- Interpretable depression assessment using a large language model.PLOS digital health · 2026Article
- A dual-stream temporal attention LSTM with visual feature enhancement for depression screening in incarcerated populations.Frontiers in psychology · 2026Article
- Article
- Multimodal behavioral phenotyping for depressive-spectrum classification and severity estimation using eye tracking, facial behavior, and transcript-derived language.Frontiers in psychiatry · 2026Article
- Depression detection using deep learning and large language models from multimodalities.Frontiers in digital health · 2026Review
- Personalized vs. population-based speech models for multi-dimensional mental health prediction.Frontiers in digital health · 2026Article
- Ecological Momentary Assessment and Voice-Informed Forecast and Detection for the Diagnosis of Major Depression.Advances in experimental medicine and biology · 2026Review
- Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study.JMIR medical informatics · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model. Additionally, facial data extracted from video frames is integrated with textual data to create a multimodal model for depression severity prediction. We evaluate three approaches: text-based features, facial features, and a combination of both. Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. This study underscores the potential of automated depression detection, showing text-only models as robust and effective while paving the way for multimodal analysis.
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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.