Depression Severity Estimation

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Overview

Depression severity estimation aims to measure symptom levels for screening, clinical decision support, and tracking change over time, often against scales such as the PHQ-8/9 or MADRS. Approaches range from statistical models of questionnaire and behavioral features to machine learning on speech, text, video, and wearable data, as well as large language models that score interviews or conversations; multimodal fusion and symptom-guided attention are common strategies. Evaluation uses clinical interview datasets and benchmarks such as DAIC-WOZ and AVEC, but performance can shift across languages, populations, interview protocols, and assessment scales. A central tension is that strong agreement with labels does not by itself establish clinical validity: calibration, evidence faithfulness, cross-context generalization, and reliable measurement of change remain open challenges.

Latest papers

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35 papers9 papers in the last four weeks, up 80% on the four weeks before. 0.1% of all new papers.

  1. Words Are the Voice of the Mind: Depression Severity Estimation from Social Traces via Mental-Guided Evidence Reasoning

    Oct 24, 2026Nada Xu, Xueqin Chen, Qiang Gao +2Depression Severity Estimation

    Sichuan University · Southwestern University of Finance and Economics

  2. EviDial: When Correct Depression Predictions Lack Evidence

    Oct 20, 2026Junjie Xu, Jin Zhu, Yuzhe Yang +6Clinical Language Model EvaluationDepression Severity Estimation

    East China Normal University · Fudan University

  3. Task-evoked eye movements as candidate digital biomarkers of depression severity in adolescents and young adults

    Oct 13, 2026Youxiang Sun, Yujie Nie, Zhuoru Wu +6Depression Severity EstimationEye Tracking

    School of Control Science and Engineering, Shandong University, Jinan, Shandong, China · Engineering Research Center of Intelligent Unmanned System, Ministry of Education, China · Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China +1

  4. Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity

    Oct 10, 2026Hsiang-Chen Yeh, Aurosweta Mahapatra, Shreeram Suresh Chandra +2Speech ProsodyClinical Speech Processing

    Clinical Mental Health Counseling, Johns Hopkins University, Baltimore, MD, USA · Center for Language and Speech Processing (CLSP), Johns Hopkins University, Baltimore, MD, USA · College of Connected Computing, Vanderbilt University, Nashville, TN, USA

  5. HiPAD: A Unified Gait, Audio-Visual, and Personality Fusion Network for Depression Detection, with a Hierarchical Personality Ablation and the Calibration Limits of Domain-Adapted LLMs

    Oct 10, 2026Karol Chlasta, Piotr StruzikDepression DetectionDepression Severity Estimation

    Kozminski University Warsaw, Poland · WarsawIQ Warsaw, Poland

  6. MC-TRCM: Observation-Aware Recursive Fusion for Incomplete Mobile and Wearable Mental-Health Feature Views

    Oct 8, 2026Wentao Wang, Lifeng Han, Zining Ren +2Clinical Outcome PredictionClinical Prediction

    Dalian University of Technology, Dalian, China · The Hong Kong Polytechnic University, Hong Kong, China · Southwest University, Chongqing, China

  7. Language-model ratings of depression reflect the rater more than the patient

    Oct 6, 2026Baihan LinInter-Rater ReliabilityAnnotator Disagreement

    Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA · Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA · Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA +2

  8. Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity

    Sep 30, 2026Xinkai ChenDepression DetectionCoT Reasoning

    Independent Researcher

  9. MERID: Multimodal Exploration via Recursive Self-Improvement Agents for Major Depression Analysis

    Sep 28, 2026Lei Liu, Zhaokang Liang, Qingcheng Zeng +4Depression DetectionMultimodal Classification

    Yale University · Zhejiang University · Northwestern University +3

  10. Cross-Scale Transfer Learning for Depression Severity Prediction: From PHQ-8 to HAMD-17 Across Languages and Clinical Paradigms

    Sep 23, 2026Wenjie Feng, Sahba Zojaji, Satoshi NakamuraClinical Outcome PredictionDepression Severity Estimation

    School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China · School of Humanities and Social Science, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China · Shenzhen Loop Area Institute, Shenzhen, China

  11. Beyond Baseline Severity: Temporal and Disease-Specific Predictors of Depression Outcomes Following Mindfulness Interventions

    Sep 19, 2026Muhammad Jawad Chowdhury, Sultanus Salehin, Akib Jayed IslamClinical Outcome PredictionMental Health

    Department of Computer Science & Engineering, Islamic University of Technology, Bangladesh · Department of Electrical & Electronic Engineering, Islamic University of Technology, Bangladesh · Computational Colour & Spectral Imaging, Norwegian University of Science and Technology, Norway

  12. AdoDAS: A Privacy-Preserving Multimodal Challenge for Adolescent Depression, Anxiety, and Stress Assessment

    Sep 7, 2026Zhaojie Luo, Junkun Wang, Tianhua Qi +11Depression DetectionMental Health

    Southeast University · Shenzhen Loop Area Institute · Southeast University Nanjing, Jiangsu, China +13

  13. LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues

    Sep 3, 2026Jiayi Li, Zhaomin Wu, Bingsheng HeMental Health CounselingDepression Detection

    Department of Computer Science National University of Singapore

  14. Do Depressive Facial Patterns Transfer Across Cultures and Contexts? Evidence from a German RCT and E-DAIC

    Sep 2, 2026Misha Sadeghi, Robert Richer, Lydia Helene Rupp +7Depression DetectionCross-Dataset Generalization

    Dept. AIBE, FAU Erlangen-Nürnberg Munich Center for Machine Learning Germany · Dept. of Clinical Psychology and Psychotherapy, FAU Erlangen-Nürnberg Germany · Dept. of Computer Science, FAU Erlangen-Nürnberg Germany +1

  15. HiMA-MDD: A Hierarchical Multi-Agent Harness for Interpretable Multimodal Depression Detection in Clinical Interviews

    Aug 22, 2026Ao Chen, Xiaojiang PengDepression DetectionDepression Severity Estimation

    School of Artificial Intelligence, Shenzhen Technology University

  16. The MADRS Pipeline: Supporting Depression Assessment in Clinical Trials

    Jul 30, 2026Mila Fodor, Katalin Ócsai, Francesco Periti +2Depression DetectionClinical Decision Support

    Clario, part of Thermo Fisher Scientific

  17. DynaBridge: Dynamic Summary-Guided Cross-Task Multimodal Fusion for DASS-Structured Mental Health Assessment

    Jul 28, 2026Shiyu Teng, Haichen Yu, Jiaqing Liu +6Medical DiagnosisDepression Severity Estimation

    College of Information Science and Engineering Ritsumeikan University Ibaraki, Osaka, Japan · College of Computer Science and Technology Zhejiang University Hangzhou, Zhejiang, China

  18. DS@GT ARC at eRisk 2026: Hybrid Multi-Agent LLM System with Structured Algorithmic Guidance for Conversational Depression Screening

    Jul 18, 2026Victor Gong, David GuechaMulti-Agent LLM SystemsDepression Detection

    Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332

  19. Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis

    Jun 24, 2026Felipe Moreno, Sharifa Alghowinem, Hae Won Park +1Depression DetectionNeural Network Interpretability

    Media Lab MIT Cambridge, USA

  20. A Multi-Agent Audit Framework for High-Stakes Reasoning: Evaluation and Interpretability in Clinical Mental Health Screening

    Jun 19, 2026Jingchen Ye, Yanpei Yu, Luyao ZhangLLM AuditingLLM Interpretability

    Duke Kunshan University Suzhou, China · Social Science Division and Digital Innovation Research Center, Duke Kunshan University, No.8 Duke Ave., Kunshan, Jiangsu 215316, China.

  21. Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language

    Jun 17, 2026Yunkai Xu, Saeed AbdullahMultilingual Language Model EvaluationDepression Detection

    Pennsylvania State University, United States

  22. 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 DetectionMental Health

    1TH Nürnberg · 2FAU Erlangen · 3PMU Klinikum Nürnberg, Germany

  23. Fine-tuning LLMs for Passive Depression Severity Estimation from AI Mental Health Dialogue

    Jun 16, 2026Olivier Tieleman, Ziyi Zhu, Ting Su +3Depression DetectionLLM Fine-Tuning

  24. Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care

    May 23, 2026Vassilis Lyberatos, Edmund G. Dervakos, Eleni Adamidi +2Depression DetectionMental Health

    1National Technical University of Athens, Athens, Greece · 2PsychNow

  25. EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation

    May 21, 2026Zhaomin Wu, Jiayi Li, Bingsheng HeMental Health CounselingDepression Detection

    Department of Computer Science National University of Singapore

  26. Can We Trust LLMs for Mental Health Screening? Consistency, ASR Robustness, and Evidence Faithfulness

    May 10, 2026Erfan Loweimi, Sofia de la Fuente Garcia, Samira Loveymi +2HealthcareMental Health

    Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK · Department of Computer Engineering, Ahvaz Campus, Islamic Azad University, Ahvaz, Iran · School of Health and Social Care, Edinburgh Napier University, Edinburgh, UK

  27. ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms

    May 4, 2026Alexandria K. Vail, Marcelo Cicconet, Katie Aafjes-van Doorn +2Depression DetectionMental Health

    Deliberate Solutions, Inc. New York, New York, USA

  28. MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation

    Apr 20, 2026Xuzhi Wang, Xinran Wu, Ziping Zhao +2Memory-Augmented Neural NetworksDepression 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

  29. EviDep: Uncertainty-Aware Multimodal Depression Estimation via Disentangled Evidential Learning

    Apr 17, 2026Fangyuan Liu, Sirui Zhao, Yangsong Zhang +5Disentangled Representation LearningEvidential 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

  30. Dynamic Summary Generation for Interpretable Multimodal Depression Detection

    Apr 13, 2026Shiyu Teng, Jiaqing Liu, Hao Sun +6Depression DetectionDepression Severity Estimation

    College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · Faculty of Engineering, University of the Ryukyus, Japan