Depression Detection

Latest papers 73

6 papers in the last four weeks, down 45% on the four weeks before. 0.0% of all new papers.

Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers
Jun 23, 2026Ahnaf Atef Choudhury, Md. Parvej Hoque Palash, Shahriar Siddique Ayon +2Healthcare Depression Detection
Department of Information Sciences and Technology George Mason University USA · Department of Computer Science and Engineering Jahangirnagar University Bangladesh · Department of Computer Science AIUB Bangladesh +2
Towards Transparent Mental Health Insights: An Explainable AI Model for Career-Related Depression and Anxiety Among University Students Using Structured Data
Jun 19, 2026Arsham Azam, Rasikh Ali, Tayyaba Farhat +1Explainable Artificial Intelligence Depression Detection
Faculty of Computer Science and Information Technology, The Superior University, Lahore 54600, Pakistan · Intelligent Data Visual Computing Research (IDVCR), Lahore 54600, Pakistan · Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 12571, Saudi Arabia
Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language
Jun 17, 2026Yunkai Xu, Saeed AbdullahMultilingual Language Model Evaluation Depression Detection
Pennsylvania State University, United States
Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews
Jun 16, 2026Franziska Braun, Alea Rüggeberg, Thomas Ranzenberger +4Depression Detection Mental Health
1TH Nürnberg · 2FAU Erlangen · 3PMU Klinikum Nürnberg, Germany
Fine-tuning LLMs for Passive Depression Severity Estimation from AI Mental Health Dialogue
Jun 16, 2026Olivier Tieleman, Ziyi Zhu, Ting Su +3Depression Detection LLM Fine-Tuning
End-to-End Machine Learning for Depressive State Classification via EEG and fNIRS
Jun 10, 2026Riki Sakurai, Simon Kojima, Mihoko Otake-Matsuura +2Depression Detection
Graduate School of Engineering and Science Shibaura Institute of Technology, Tokyo, Japan · RIKEN AIP, Tokyo, Japan · Inria Centre at the University of Bordeaux Talence, France +4
Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning
Jun 9, 2026Yiqing Lyu, Xianbing Zhao, Buzhou Tang +1LLM Inference Depression Detection
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China · School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China, Harbin Institute of Technology, Shenzhen, Guangdong, China, and Guangdong Provincial Key Laboratory of Intelligent Information Processing · School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China, Pengcheng Laboratory, Guangdong Provincial Key Laboratory of Intelligent Information Processing +1
InfoShield: Privacy-Preserving Speech Representations for Mental Health Screening via Information-Theoretic Optimization
Jun 4, 2026Xueyang Wu, Siyuan Liu, Kezhuo Yang +1Audio Representation Learning Depression Detection
Shenzhen NeurStar Inc., China · University of York, United Kingdom · Shanghai Jiao Tong University, China
Beyond Augmentation: Score-Guided Pathological Prior for EEG-based Depression Detection
May 29, 2026Xiaojing Chen, Jingqi Cheng, Xu Zhao +2Electroencephalography Depression Detection
School of Computer Science and Technology, Hefei University of Technology Hefei, China · School of Computer Science and Information Engineering, Hefei University of Technology Hefei, China
Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection
May 27, 2026Antonia Šarčević, Nikolina FridGradient-Based Attribution Electroencephalography
University of Zagreb Faculty of Electrical Engineering and Computing, Croatia
Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care
May 23, 2026Vassilis Lyberatos, Edmund G. Dervakos, Eleni Adamidi +2Depression Detection Mental Health
1National Technical University of Athens, Athens, Greece · 2PsychNow
Distance-Aware Joint Spatio-Temporal Graph Contrastive Learning for Major Depressive Disorder Diagnosis
May 22, 2026Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1Functional Connectivity Depression Detection
School of Information Technology and Communication, Griffith University, Australia
fMRI-Diffusion: Generating fMRI Time Series Via a Temporal Transformer Diffusion Model for Major Depressive Disorder Diagnosis
May 22, 2026Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1Depression Detection Time Series Generation
aSchool of Information and Communication Technology, Griffith University, Gold Coast, QLD, 4215, Australia
EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation
May 21, 2026Zhaomin Wu, Jiayi Li, Bingsheng HeMental Health Counseling Depression Detection
Department of Computer Science National University of Singapore
FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data
May 18, 2026Nuredin Ali Abdelkadir, Anjali Ratnam, Zeerak Talat +1Depression Detection Mental Health
University of Minnesota · University of Edinburgh
Explainable Detection of Depression Status Shifts from User Digital Traces
May 14, 2026Loris Belcastro, Francesco Gervino, Fabrizio Marozzo +2Social Media Analysis Depression Detection
DIMES, University of Calabria, Ponte P. Bucci 41/C, 87036 Rende, Italy
A Multi-Probe Audit of Clinical-Interview Depression Detection Benchmarks
May 13, 2026Takehiro Ishikawa, Jon DukeDepression Detection Mental Health
College of Computing, Georgia Institute of Technology, Atlanta, GA, USA · Georgia Tech Research Institute, Georgia Institute of Technology, Atlanta, GA, USA
New AI-Driven Tools for Enhancing Campus Well-being: A Prevention and Intervention Approach
May 11, 2026Jinwen TangReinforcement Learning Depression Detection
University of Missouri-Columbia[cs.AI]
Voice Biomarkers for Depression and Anxiety
May 11, 2026Oleksii Abramenko, Noah D. Stein, Colin VazAudio Representation Learning Depression Detection
machine learning team at Kintsugi Mindful Wellness, Inc.
ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms
May 4, 2026Alexandria K. Vail, Marcelo Cicconet, Katie Aafjes-van Doorn +2Depression Detection Mental Health
Deliberate Solutions, Inc. New York, New York, USA
Multi-Level Narrative Evaluation Outperforms Lexical Features for Mental Health
Apr 30, 2026Yuxi Ma, Jieming Cui, Muyang Li +6Document Understanding Depression Detection
Institute for Artificial Intelligence, Peking University · School of Psychological and Cognitive Sciences, Peking University · School of Intelligence Science and Technology, Peking University +3
Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech
Apr 29, 2026Himadri S SamantaDepression Detection
Independent Researcher, Austin, Texas, USA
Entropy-Dominated Temporal Vocal Dynamics as Digital Biomarkers for Depression Detection
Apr 29, 2026Himadri S SamantaDepression Detection Mental Health
Independent Researcher, Austin, Texas, USA
Learning Evidence of Depression Symptoms via Prompt Induction
Apr 27, 2026Eliseo Bao, Anxo Perez, David Otero +1Depression Detection Mental Health
IRLab, CITIC, Universidade da Coruña A Coruña, Spain
Psychologically-Grounded Graph Modeling for Interpretable Depression Detection
Apr 27, 2026Rishitej Reddy Vyalla, Kritarth Prasad, Avinash Anand +4Depression Detection Dynamic Graph Learning
IIIT Delhi, New Delhi, India · Singapore Institute of Technology, Singapore · Nanyang Technological University, Singapore +3
FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment
Apr 26, 2026Sophie Chiang, Tom Brennan, Fethiye Irmak Dogan +2Depression Detection Medical Diagnosis
Department of Computer Science & Technology, University of Cambridge. · Harvard University.
K-SENSE: A Knowledge-Guided Self-Augmented Encoder for Neuro-Semantic Evaluation of Mental Health Conditions on Social Media
Apr 26, 2026Vijay YadavSocial Media Analysis Knowledge Augmentation for Language Models
School of Psychology, University of New South Wales Sydney, Australia
Depression Risk Assessment in Social Media via Large Language Models
Apr 21, 2026Giorgia Gulino, Manuel PetrucciSocial Media Analysis Depression Detection
1Guglielmo Marconi University, Department of Human Sciences, Rome, Italy
MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation
Apr 20, 2026Xuzhi Wang, Xinran Wu, Ziping Zhao +2Memory-Augmented Neural Networks Depression Detection
School of Computer and Information Engineering, Tianjin Normal University (TJNU), Tianjin 300387, China · Department of Automation, Tsinghua University, Beijing 100084, China · Chair of Health Informatics, Technical University of Munich, Munich, Germany +1
EviDep: Uncertainty-Aware Multimodal Depression Estimation via Disentangled Evidential Learning
Apr 17, 2026Fangyuan Liu, Sirui Zhao, Yangsong Zhang +5Disentangled Representation Learning Evidential Deep Learning
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China · aSchool of Computer Science and Technology, University of Science and Technology of China, Hefei, China · bSchool of Computer Science and Technology, Laboratory for Brain Science and Artificial Intelligence, Southwest University of Science and Technology, Mianyang, China +1

Jun 23, 2026J/K move · Enter open · S save

Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers

Ahnaf Atef Choudhury, Md. Parvej Hoque Palash, Shahriar Siddique Ayon, Ramkrishna Saha+1

Department of Information Sciences and Technology George Mason University USA · Department of Computer Science and Engineering Jahangirnagar University Bangladesh · Department of Computer Science AIUB Bangladesh+2

Healthcare Depression Detection Mental Health Clinical Risk Prediction

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One of the significant mental health issues affecting female sex workers (FSWs) is mental disorders, especially depression. Exposure to violence, stigma, and economic hardship further increases their psychological risk. Current machine learning (ML) models are typically ineffective at capturing the high-dimensional and complex risk patterns that exist in this marginalized group. This paper suggests a hybrid predictive model that merges an ensemble feature selection strategy using ANOVA and mutual information and Harris Hawks optimization-tuned logistic regression and represents a new application of swarm intelligence to predict mental health in vulnerable groups. The explainable AI (XAI) methods can be used to understand the factors of trauma associated with model predictions. When applied to a group of 3,005 FSWs, it can be seen that the proposed model is more effective than traditional classifiers, with an accuracy of 95.78%, an F1 score of 95.77%, and an AUC of 0.96, and identifying post-traumatic stress, client-related violence, and occupational factors as major contributors to depression. This work bridges the gaps between conventional and ML approaches to develop an XAI tool that enables vulnerable groups to receive early assistance, evidence-based targeted psychosocial care, and health planning.