Copertina

DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ibrahimov, Yusif, Anwar, Tarique, Yuan, Tommy
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment
di: Ibrahimov, Yusif, et al.
Pubblicazione: (2025)

Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook
di: Ibrahimov, Yusif, et al.
Pubblicazione: (2024)

Deep Knowledge-Infusion For Explainable Depression Detection
di: Dalal, Sumit, et al.
Pubblicazione: (2024)

Neurocircuitry-Inspired Hierarchical Graph Causal Attention Networks for Explainable Depression Identification
di: Chen, Weidao, et al.
Pubblicazione: (2025)

Leveraging Large Language Models for Cost-Effective, Multilingual Depression Detection and Severity Assessment
di: Xian, Longdi, et al.
Pubblicazione: (2025)

Bridging Accuracy and Explainability in EEG-based Graph Attention Network for Depression Detection
di: Hazra, Soujanya, et al.
Pubblicazione: (2025)

Contrastive Cross-Modal Learning for Infusing Chest X-ray Knowledge into ECGs
di: Punyamoorty, Vineet, et al.
Pubblicazione: (2025)

Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity
di: Tokareva, Anastasiia, et al.
Pubblicazione: (2025)

Wearable-Derived Behavioral and Physiological Biomarkers for Classifying Unipolar and Bipolar Depression Severity
di: Ouzar, Yassine, et al.
Pubblicazione: (2025)

AI Enabled User-Specific Cyberbullying Severity Detection with Explainability
di: Prama, Tabia Tanzin, et al.
Pubblicazione: (2025)

Conformal Depression Prediction
di: Li, Yonghong, et al.
Pubblicazione: (2024)

Explainable Detection of Depression Status Shifts from User Digital Traces
di: Belcastro, Loris, et al.
Pubblicazione: (2026)

Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection
di: Šarčević, Antonia, et al.
Pubblicazione: (2026)

Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda
di: Kimera, Richard, et al.
Pubblicazione: (2024)

MOGAM: A Multimodal Object-oriented Graph Attention Model for Depression Detection
di: Cha, Junyeop, et al.
Pubblicazione: (2024)

AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data
di: Soley, Nidhi, et al.
Pubblicazione: (2025)

Voice Biomarkers for Depression and Anxiety
di: Abramenko, Oleksii, et al.
Pubblicazione: (2026)

ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media
di: Agarwal, Aakash Kumar, et al.
Pubblicazione: (2025)

Fair Uncertainty Quantification for Depression Prediction
di: Li, Yonghong, et al.
Pubblicazione: (2025)

Retrieval-Augmented Multimodal Depression Detection
di: Hou, Ruibo, et al.
Pubblicazione: (2025)

EEG Based Generative Depression Discriminator
di: Mao, Ziming, et al.
Pubblicazione: (2024)

On the Validity of Head Motion Patterns as Generalisable Depression Biomarkers
di: Gahalawat, Monika, et al.
Pubblicazione: (2025)

Synthetic Data Generation with LLM for Improved Depression Prediction
di: Kang, Andrea, et al.
Pubblicazione: (2024)

LLM Assistance for Pediatric Depression
di: Ignashina, Mariia, et al.
Pubblicazione: (2025)

Robust and Explainable Depression Identification from Speech Using Vowel-Based Ensemble Learning Approaches
di: Feng, Kexin, et al.
Pubblicazione: (2024)

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach
di: Ahmed, Md Sabbir, et al.
Pubblicazione: (2025)

A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention
di: Li, Shengjie, et al.
Pubblicazione: (2024)

Context-Aware Deep Learning for Multi Modal Depression Detection
di: Lam, Genevieve, et al.
Pubblicazione: (2024)

LINKER: Learning Interactions Between Functional Groups and Residues With Chemical Knowledge-Enhanced Reasoning and Explainability
di: Pham, Phuc, et al.
Pubblicazione: (2025)

Attention Sinks and Outliers in Attention Residuals
di: Luo, Haozheng, et al.
Pubblicazione: (2026)

Test-Time Training for Depression Detection
di: Dumpala, Sri Harsha, et al.
Pubblicazione: (2024)

TimeMKG: Knowledge-Infused Causal Reasoning for Multivariate Time Series Modeling
di: Sun, Yifei, et al.
Pubblicazione: (2025)

On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts
di: Ahmed, Mustofa, et al.
Pubblicazione: (2024)

Incomplete Depression Feature Selection with Missing EEG Channels
di: Gong, Zhijian, et al.
Pubblicazione: (2025)

Neural Responses to Affective Sentences Reveal Signatures of Depression
di: Kommineni, Aditya, et al.
Pubblicazione: (2025)

Analyzing the Temporal Factors for Anxiety and Depression Symptoms with the Rashomon Perspective
di: Cavus, Mustafa, et al.
Pubblicazione: (2026)

Automatic Depression Assessment using Machine Learning: A Comprehensive Survey
di: Song, Siyang, et al.
Pubblicazione: (2025)

STEP-PD: Stage-Aware and Explainable Parkinson's Disease Severity Classification Using Multimodal Clinical Assessments
di: Islam, Md Mezbahul, et al.
Pubblicazione: (2026)

U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection
di: Cheong, Jiaee, et al.
Pubblicazione: (2025)

A Model-Mediated Stacked Ensemble Approach for Depression Prediction Among Professionals
di: Ahmmed, Md. Mortuza, et al.
Pubblicazione: (2025)