ArticleIEEE journal of biomedical and health informatics2024
Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns.
Article in IEEE journal of biomedical and health informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Eye-Tracking Phenotypes Across the Depressive Spectrum: Joint Modeling of Diagnostic Group and Symptom Severity.CNS neuroscience & therapeutics · 2026Article
- Identification of suicidal ideation in major depressive disorder: A machine learning approach with multimodal digital features.PLOS digital health · 2026Article
- Detecting cognitive impairment and psychological well-being among older adults.Machine learning. Health · 2026Article
- An algorithm for optimizing psychological regulation strategies for college students based on image recognition and reinforcement learning.Scientific reports · 2026Article
- Patient-friendly simplification and translation of neuroradiology impressions using artificial intelligence.Scientific reports · 2026Article
- Computer-Assisted Performance-Based Assessment for Mental Health: A Scoping Review.PsyCh journal · 2026Article
- A Conversational Platform (Okaya) for Multimodal Digital Biomarkers of Fatigue, Cognition, and Mental Health: Feasibility Observational Study.JMIR formative research · 2026Observational
- From synapse to system: mechanistic pathways of neural signaling dysfunction in psychiatric disorders.Frontiers in cell and developmental biology · 2026Review
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- Estimating presenteeism from repeated smartphone-based multimodal behavioral responses.PloS one · 2026Article
- Identifying psychiatric manifestations in outpatients with depression and anxiety: a large language model-based approach.Npj mental health research · 2025Article
- A literature review of remote mental health screening: barriers, potential solutions, and tools.Frontiers in digital health · 2025Review
- Oscillatory components of bidirectional cardio-respiratory coupling in depression and suicidal ideation: insights from swarm decomposition and entropy analysis.Frontiers in network physiology · 2025Article
- Multimodal Technologies for Remote Assessment of Neurological and Mental Health.Journal of speech, language, and hearing research : JSLHR · 2024Review
- A portable and efficient dementia screening tool using eye tracking machine learning and virtual reality.NPJ digital medicine · 2024Article
- Evaluating and mitigating unfairness in multimodal remote mental health assessments.PLOS digital health · 2024Article
- Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivePsychiatric evaluation suffers from subjectivity and bias, and is hard to scale due to intensive professional training requirements. In this work, we investigated whether behavioral and physiological signals, extracted from tele-video interviews, differ in individuals with psychiatric disorders.
methodsTemporal variations in facial expression, vocal expression, linguistic expression, and cardiovascular modulation were extracted from simultaneously recorded audio and video of remote interviews. Averages, standard deviations, and Markovian process-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination.
resultsStatistically significant feature differences were found between psychiatric and control subjects. Correlations were found between features and self-rated depression and anxiety scores. Heart rate dynamics provided the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities provided AUROCs of 0.72-0.82.
conclusionMultimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status. SIGNIFICANCE: The proposed multimodal approach has the potential to facilitate scalable, remote, and low-cost assessment for low-burden automated mental health services.
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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.