Deep Learning and Large Language Models for Audio and Text Analysis in Predicting Suicidal Acts in Chinese Psychological Support Hotlines
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；Beijing Suicide Research and Prevention Center, Beijing Huilongguan Hospital（北京回龙观医院北京自杀研究与预防中心）；WHO Collaborating Center for Research and Training in Suicide Prevention（世界卫生组织自杀预防研究与培训合作中心）；Peking University Huilongguan Clinical Medical School（北京大学回龙观临床医学院）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑研究所（ICM））；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié Salpêtrière（萨尔皮特里耶医院）
Suicide is a pressing global issue, demanding urgent and effective preventive interventions. Among the various strategies in place, psychological support hotlines had proved as a potent intervention method. Approximately two million people in China attempt suicide annually, with many individuals making multiple attempts. Prompt identification and intervention for high-risk individuals are crucial to preventing tragedies. With the rapid advancement of artificial intelligence (AI), especially the development of large-scale language models (LLMs), new technological tools have been introduced to the field of mental health. This study included 1284 subjects, and was designed to validate whether deep learning models and LLMs, using audio and transcribed text from support hotlines, can effectively predict suicide risk. We proposed a simple LLM-based pipeline that first summarizes transcribed text from approximately one hour of speech to extract key features, and then predict suicidial bahaviours in the future. We compared our LLM-based method with the traditional manual scale approach in a clinical setting and with five advanced deep learning models. Surprisingly, the proposed simple LLM pipeline achieved strong performance on a test set of 46 subjects, with an F1 score of 76\% when combined with manual scale rating. This is 7\% higher than the best speech-based deep learning models and represents a 27.82\% point improvement in F1 score compared to using the manual scale apporach alone. Our study explores new applications of LLMs and demonstrates their potential for future use in suicide prevention efforts.
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明责任人工智能与数据分析实验室）；King Abdullah University of Science and Technology（卡瓦尔大学科学与技术大学）；Washington University in St.Louis（圣路易斯华盛顿大学）；University of Macau（澳门大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Speech-based Psychological Crisis Assessment using LLMs
Tsinghua University（清华大学）；Peking University Huilongguan Clinical Medical School（北京大学回龙guan临床医学院）；WHO Collaborating Centre for Research and Training in Suicide Prevention（世界卫生组织自杀预防研究与培训协作中心）
Deep Learning-Based Feature Fusion for Emotion Analysis and Suicide Risk Differentiation in Chinese Psychological Support Hotlines
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；Peking University Huilongguan Clinical Medical School（北京大学回龙湖临床医学院）；Sorbonne Université（索邦大学）；Institut du Cerveau – Paris Brain Institute - ICM（巴黎脑研究所（ICM））；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié-Salpêtrière（皮蒂-萨尔皮特里医院）
Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons
Institute of Psychology, Chinese Academy of Sciences（中国科学院心理研究所）；Institute of Population Research, Peking University（北京大学人口研究所）；University of Puget Sound（普吉特湾大学）
Psychological Assessments with Large Language Models: A Privacy-Focused and Cost-Effective Approach
Harvard-Smithsonian Center for Astrophysics（哈佛-史密森尼天体物理中心）；Laboratoire de Recherche en Neuroimagerie（神经成像研究实验室）；University Hospital (CHUV)（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）
Fine-grained Speech Sentiment Analysis in Chinese Psychological Support Hotlines Based on Large-scale Pre-trained Model
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；Peking University Huilongguan Clinical Medical School（北京大学回龙湖临床医学院）；Sorbonne Université（索邦大学）；Institut du Cerveau – Paris Brain Institute - ICM（巴黎脑研究所）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗援助医院）；Hôpital de la Pitié-Salpêtrière（皮蒂-萨尔佩特里埃医院）
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；School of Nursing, Wuhan University（武汉大学护理学院）；Sorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière（索邦大学、巴黎脑研究所、法国国家科学研究中心、法国国家计算机与自动化研究所、法国国家健康与医学研究院、巴黎公立医院集团、皮蒂-萨尔皮特里耶医院）
SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；School of Nursing, Wuhan University（武汉大学护理学院）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（大脑研究所-巴黎脑研究所-ICM）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié Salpêtrière（皮蒂-萨尔皮特里医院）
DelusionEval: Measuring Delusion-Linked Behaviors in AI Chatbots
Stanford University（斯坦福大学）；University of Chicago（芝加哥大学）；Carnegie Mellon University（卡内基梅隆大学）；Harvard University（哈佛大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals
Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑科学院）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公立医院集团）；Hôpital de la Pitié Salpêtrière（萨尔佩特里埃医院）；Beijing University of Technology（北京工业大学）；Wuhan University（武汉大学）；Wuhan Mental Health Center（武汉市精神卫生中心）；Wuhan Hospital for Psychotherapy（武汉市心理医院）
Two-Stage Voting for Robust and Efficient Suicide Risk Detection on Social Media
Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Informatics and Networked Systems（信息与网络系统系）；University of Pittsburgh（匹兹堡大学）；Department of Psychiatry（精神病学系）；Tandon School of Engineering（塔能工程学院）；New York University（纽约大学）
CARE: Counselor-Aligned Response Engine for Online Mental-Health Support
Ben-Gurion University（本· Gurion 大学）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；School of Informatics, University of Edinburgh Edinburgh UK（爱丁堡大学信息学院爱丁堡 英国）
Protective Factor-Aware Dynamic Influence Learning for Suicide Risk Prediction on Social Media
IEEE Publication Technology Department（IEEE出版技术部）；Department of Computing（计算系）；The Hong Kong Polytechnic University（香港理工大学）；Department of Computer Science, Technology（计算机科学与技术系）；Tsinghua University（清华大学）；Department of Social and Behavioural Sciences（社会科学与行为科学系）；City University of Hong Kong（香港城市大学）
Characterizing Delusional Spirals through Human-LLM Chat Logs
Stanford University（斯坦福大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Chicago（芝加哥大学）；Independent Researcher（独立研究者）；Harvard Belfer Center（哈佛贝尔弗中心）；University of Minnesota（明尼苏达大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Augmenting Rating-Scale Measures with Text-Derived Items Using the Information-Determined Scoring (IDS) Framework
Psychometrics Centre, Judge Business School, University of Cambridge（剑桥大学心理测量中心、剑桥大学商学院）；Faculty of Law, University of Cambridge（剑桥大学法学院）；School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院）；Faculty of Psychology, Beijing Normal University（北京师范大学心理学学院）
The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youth
Baruch Ivcher School of Psychology, Reichman University（赖希曼大学巴鲁克·伊夫切尔心理学院）；The Education Department, Ariel University（阿里尔大学教育系）；Rambam Medical Center（兰巴姆医疗中心）；Faculty of Data and Decision Sciences, Technion, IIT（以色列理工学院数据与决策科学学院）
