Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time
Based on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. Without a cure, clinical prognostication and early intervention represent the most effective ways to delay its progression. To this end, Artificial Intelligence and computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, and treatment. However, traditional approaches need more semantic knowledge management and explicability capabilities. Moreover, using Large Language Models (LLMs) for cognitive decline diagnosis is still scarce, even though these models represent the most advanced way for clinical-patient communication using intelligent systems. Consequently, we leverage an LLM using the latest Natural Language Processing (NLP) techniques in a chatbot solution to provide interpretable Machine Learning prediction of cognitive decline in real-time. Linguistic-conceptual features are exploited for appropriate natural language analysis. Through explainability, we aim to fight potential biases of the models and improve their potential to help clinical workers in their diagnosis decisions. More in detail, the proposed pipeline is composed of (i) data extraction employing NLP-based prompt engineering; (ii) stream-based data processing including feature engineering, analysis, and selection; (iii) real-time classification; and (iv) the explainability dashboard to provide visual and natural language descriptions of the prediction outcome. Classification results exceed 80 % in all evaluation metrics, with a recall value for the mental deterioration class about 85 %. To sum up, we contribute with an affordable, flexible, non-invasive, personalized diagnostic system to this work.
EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety
Department of Electrical & Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）；Department of Computer Science, Princeton University（普林斯顿大学计算机科学系）；Department of Computer Science & Engineering, University of Michigan（密歇根大学计算机科学与工程系）；Department of Data Science & Engineering, University of Michigan（密歇根大学数据科学与工程系）；Department of Philosophy, Columbia University（哥伦比亚大学哲学系）；AI Lab, Princeton University（普林斯顿大学人工智能实验室）；Chen Frontier Lab for Al and Mental Health, Tianqiao and Chrissy Chen Institute（天桥及克里斯西·陈研究所人工智能与心理健康前沿实验室）；Theta Health Inc.（Theta健康公司）
Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice
Euler Institute, Università della Svizzera italiana（欧拉研究所，瑞士大学）；National Institute of Informatics（国家信息研究所）；Brown University（布朗大学）；University of Tübingen（图宾根大学）；Insubria University（因斯布里亚大学）
Towards Interpretable Mental Health Analysis with Large Language Models
The University of Manchester（曼彻斯特大学）；University of Helsinki（赫尔辛基大学）；Yale University（耶鲁大学）；Artificial Intelligence Research Center, AIST（日本国立研究开发法人产业技术综合研究所人工智能研究中心）；Jiangxi Normal University（江西师范大学）
DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods
Leiden Institute of Advanced Computer Science (LIACS), Leiden University（莱顿高级计算机科学研究所（LIACS），莱顿大学）；School of Computing and Communications (SCC), Lancaster University（计算与通信学院（SCC），兰卡斯特大学）；Department of Medical Informatics, Erasmus University Medical Center Rotterdam（医学信息学系，埃因霍温医学中心鲁特万分校）；Biomedical Data Sciences, Leiden University Medical Center（生物医学数据科学，莱顿大学医学中心）
Application of Machine Learning algorithms to the medical domain is an emerging trend that helps to advance medical knowledge. At the same time, there is a significant a lack of explainable studies that promote informed, transparent, and interpretable use of Machine Learning algorithms. In this paper, we present explainable multi-class classification of the Covid-19 mental health data. In Machine Learning study, we aim to find the potential factors to influence a personal mental health during the Covid-19 pandemic. We found that Random Forest (RF) and Gradient Boosting (GB) have scored the hig…
ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data
California Institute of Technology（加州理工学院）；UCLA（加州大学洛杉矶分校）；National University of Singapore（新加坡国立大学）；Hangzhou Dianzi University（杭州电子科技大学）；Fudan University（复旦大学）
Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models
Department of Computer Science, Stony Brook University（石英布大学计算机科学系）；Oslo Metropolitan University, Oslo Business School（奥斯陆 Metropolitan 大学奥斯陆商学院）；University of Texas at Dallas, Dept. of Psychology（德克萨斯大学达拉斯分校心理学系）
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review
Faculty of Computer Science and Engineering, Shahid Beheshti University（沙希德·贝赫什提大学计算机科学与工程学院）
The Linguistic Architecture of Reflective Thought: Evaluation of a Large Language Model as a Tool to Isolate the Formal Structure of Mentalization
University of Pavia（帕维亚大学）；Digital Transformation Institute（数字化转型研究所）；Psychoanalytic Academy of Italian-Speaking Switzerland（意大利语区瑞士精神分析学院）；Psychiatric Services of the Canton of Grisons（格劳宾登州精神病学服务中心）
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model
Eindhoven University of Technology（埃因霍温理工大学）；Eindhoven Artificial Intelligence Systems Institute（埃因霍温人工智能系统研究所）；Ca’ Foscari University of Venice（威尼斯大学）
The George Washington University（乔治华盛顿大学）；Parenthetic, LLC（括弧有限责任公司）
There is increased interest in assisting non-expert audiences to effectively interact with machine learning (ML) tools and understand the complex output such systems produce. Here, we describe user experiments designed to study how individual skills and personality traits predict interpretability, explainability, and knowledge discovery from ML generated model output. Our work relies on Fuzzy Trace Theory, a leading theory of how humans process numerical stimuli, to examine how different end users will interpret the output they receive while interacting with the ML system. While our sample was…
College of Information Sciences and Technology, Penn State University（宾夕法尼亚州立大学信息科学与技术学院）；Department of Psychiatry and Behavioral Sciences, Emory University（埃默里大学精神病学与行为科学系）；School of Interactive Computing, Georgia Tech（佐治亚理工学院交互计算学院）；School of Psychology, Georgia Tech（佐治亚理工学院心理学学院）
Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability, due to lack of symptom modeling. This paper introduces PsySym, the first annotated symptom identification corpus of multiple psychiatric disorders, to facilitate further research progress. PsySym is annotated according to a knowledge graph of the 38 symptom classes related to 7 mental diseases complied from established clinical manuals and scales, and a novel annotation framework for diversity and quality. Experiments show that symptom-assisted MDD enabled by PsySym can outperform str…
GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text
Technological University Dublin（都柏林理工大学）；SFI Centre for Research Training in Machine Learning（SFI机器学习研究培训中心）；School of Computer Science（计算机学院）；Artificial Intelligence and Cognitive Load Research Lab（人工智能与认知负荷研究实验室）
Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion Inference
Tsinghua University（清华大学）；University of Science and Technology Beijing（北京科技大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；University of Pittsburgh（匹兹堡大学）；University of North Carolina（北卡罗来纳大学）
Decoding Workload and Agreement From EEG During Spoken Dialogue With Conversational AI
Chair of Neuroadaptive Human--Computer Interaction（神经适应性人机交互教授席位）；Brandenburg Technical University Cottbus--Senftenberg（勃兰登堡技术大学库滕堡-森芬根堡分校）；Auryal GmbH（Auryal公司）；Cottbus, Germany（库滕堡，德国）
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
Duke-NUS Medical School, National University of Singapore（新加坡国立大学杜克-国大医学院）；Singapore General Hospital（新加坡中央医院）；Singapore Health Services（新加坡保健服务集团）；Monash University（蒙纳士大学）；Duke University（杜克大学）；National University of Singapore（新加坡国立大学）
CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models
Zhejiang University（浙江大学）；Westlake University（西湖大学）；Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University（心理系，金恒毅教育学院，杭州师范大学）
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）
Natural language processing can facilitate the analysis of a person's mental state from text they have written. Previous studies have developed models that can predict whether a person is experiencing a mental health condition from social media posts with high accuracy. Yet, these models cannot explain why the person is experiencing a particular mental state. In this work, we present a new method for explaining a person's mental state from text using Monte Carlo tree search (MCTS). Our MCTS algorithm employs trained classification models to guide the search for key phrases that explain the wri…
CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis
Ertim Inalco；Milkuya Studio；Sorbonne Université（索邦大学）；IRD Lab（IRD实验室）；Faculty of Psychology, Peking University（北京大学心理学系）；Faculty of Psychology and Cognitive Science, Beijing Normal University（北京师范大学心理学与认知科学系）；Beijing Institute of Technology（北京理工大学）
Maastricht University（马斯特里赫特大学）；University of Amsterdam（阿姆斯特丹大学）
Previous research on EMA data of mental disorders was mainly focused on multivariate regression-based approaches modeling each individual separately. This paper goes a step further towards exploring the use of non-linear interpretable machine learning (ML) models in classification problems. ML models can enhance the ability to accurately predict the occurrence of different behaviors by recognizing complicated patterns between variables in data. To evaluate this, the performance of various ensembles of trees are compared to linear models using imbalanced synthetic and real-world datasets. After…
