Users of social platforms often perceive these sites as supportive spaces to post about their mental health issues. Those conversations contain important traces about individuals' health risks. Recently, researchers have exploited this online information to construct mental health detection models, which aim to identify users at risk on platforms like Twitter, Reddit or Facebook. Most of these models are centred on achieving good classification results, ignoring the explainability and interpretability of the decisions. Recent research has pointed out the importance of using clinical markers, such as the use of symptoms, to improve trust in the computational models by health professionals. In this paper, we propose using transformer-based architectures to detect and explain the appearance of depressive symptom markers in the users' writings. We present two approaches: i) train a model to classify, and another one to explain the classifier's decision separately and ii) unify the two tasks simultaneously using a single model. Additionally, for this latter manner, we also investigated the performance of recent conversational LLMs when using in-context learning. Our natural language explanations enable clinicians to interpret the models' decisions based on validated symptoms, enhancing trust in the automated process. We evaluate our approach using recent symptom-based datasets, employing both offline and expert-in-the-loop metrics to assess the quality of the explanations generated by our models. The experimental results show that it is possible to achieve good classification results while generating interpretable symptom-based explanations.
Candidate Generation and Definition-Guided Verification for Sentence-Level Depression Symptom Recognition
Institute for Systems and Robotics (ISR)（系统与机器人研究所）；Instituto Superior Técnico (IST)（高等技术学院）；University of Lisbon（里斯本大学）；Hospital Beatriz Ângelo（Beatriz Ângelo医院）
Explaining GPTs' Schema of Depression: A Machine Behavior Analysis
Stony Brook University（石溪大学）；Carnegie Mellon University（卡内基梅隆大学）；Lund University（吕勒欧大学）；Stanford University（斯坦福大学）；Institute of Human-Centered AI at Stanford University（斯坦福大学人机交互人工智能研究所）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Vanderbilt University（范德比大学）；University of Bonn（波恩大学）；The Lamarr Institute for Machine Learning & Artificial Intelligence（拉马尔人工智能与机器学习研究所）
Evaluating Lexicon Incorporation for Depression Symptom Estimation
Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC（诺曼底大学、卡昂大学、卡昂高等工程师学院、法国国家科学研究中心、格雷西研究所）；Institute of Computer Science, University of Tartu（塔尔图大学计算机科学研究所）
Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media
Hefei University of Technology（合肥工业大学）；University of Science and Technology of China（中国科学技术大学）；East China Normal University（华东师范大学）；Pennsylvania State University（宾夕法尼亚州立大学）
Interpretable Symptom Vectors for Depression in a Large Language Model
Stanford University School of Medicine（斯坦福大学医学院）；Wu Tsai Neurosciences Institute, Stanford University（斯坦福大学吴蔡神经科学研究所）；Kaiser Permanente（凯撒医疗机构）；BigCommerce（BigCommerce公司）；Veterans Affairs Palo Alto Healthcare System（帕洛阿尔托退伍军人事务医疗系统）；Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC)（塞拉太平洋精神疾病研究、教育和临床中心（MIRECC））
Psychologically-Grounded Graph Modeling for Interpretable Depression Detection
Singapore Institute of Technology（新加坡科技学院）；Nanyang Technological University（南洋理工大学）；ELLIS Institute Finland（芬兰ELLIS研究所）；University of Turku（图尔库大学）；Princess Nourah bint Abdulrahman University（普里西拉努拉·本·阿卜杜勒拉赫曼大学）
Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages
Stanford University（斯坦福大学）；Stanford School of Medicine（斯坦福医学院）；Stanford Health Care（斯坦福医疗保健系统）；Google LLC（谷歌公司）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Veterans Affairs Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗保健系统）
Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Shanghai Institute of Al for Education, East China Normal University（东华大学上海人工智能教育研究所）；School of Psychology and Cognitive Science, East China Normal University（东华大学心理学与认知科学学院）
Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis
University of Minnesota（明尼苏达大学）；University of Chicago（芝加哥大学）；Columbia University（哥伦比亚大学）；University of Virginia（弗吉尼亚大学）；University of California San Francisco（加州大学旧金山分校）；Mayo Clinic（梅奥诊所）；Institute for Health Informatics（健康信息学研究所）
Depression Detection on Social Media with Large Language Models
Department of Electronic Engineering, BNRist, Tsinghua University（电子工程系，北京理工大学，清华大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；Department of Computer Science, The University of Chicago（计算机科学系，芝加哥大学）
Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；Fujita Health University（藤田医科大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
University of Amsterdam（阿姆斯特丹大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）；National Institutes of Health（美国国立卫生研究院）；Allen Institute for AI（艾伦人工智能研究所）
From Detection to Discovery: A Closed-Loop Approach for Simultaneous and Continuous Medical Knowledge Expansion and Depression Detection on Social Media
Shenzhen University（深圳大学）；Iowa State University（爱荷华州立大学）；University of Delaware（特拉华大学）；University of Arizona（亚利桑那大学）
Semantic Similarity Models for Depression Severity Estimation
Centro de Investigación en Tecnoloxías da Información e as Comunicacións (CITIC)（信息与通信技术研究中心（CITIC））；Universidade da Coruña（拉科鲁尼亚大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心（hessian.AI））；Technical University of Darmstadt（达姆施塔特工业大学）
