ArticleMethodsX2024
Psychological disorder detection: A multimodal approach using a transformer-based hybrid model.
Article in MethodsX, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- NSX-Net: A Neurolinguistic and Acoustic Multimodal Deep Learning Framework for Speech Disorder Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Large Language Models and Their Applications in Mental Health: Scoping Review.JMIR mental health · 2026Article
- Article
- Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study.JMIR medical informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detecting psychological disorders, particularly depression, is a complex and critical task within the realm of mental health assessment. This research explores a novel approach to improve the identification of psychological distresses, such as depression, by addressing the subjectivity, complexity, and biasness inherent in traditional diagnostic techniques. Using multimodal data, such as voice characteristics and linguistic content from participant interviews, we developed a Transformer-Based Hybrid Model that combines advanced natural language processing and deep learning approaches. This model provides a complete assessment of an individual's psychological well-being by merging aural cues and textual data. This study investigates the theoretical underpinnings, technical complexities, and practical applications of this model in the context of psychological disorder detection. Additionally, the model's design and implementation details are thoroughly documented to ensure replicability by other researchers.•A unique way of strengthening emotional ailments (focusing on depression).•Transformer-Based Hybrid Model is proposed using multimodal data from interviews of participants.•The model integrates voice characteristics (aural cues) and linguistic content (textual data).•Comparative analysis of this research with existing approaches.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.