We introduce BlaBla, an open-source Python library for extracting linguistic features with proven clinical relevance to neurological and psychiatric diseases across many languages. BlaBla is a unifying framework for accelerating and simplifying clinical linguistic research. The library is built on state-of-the-art NLP frameworks and supports multithreaded/GPU-enabled feature extraction via both native Python calls and a command line interface. We describe BlaBla's architecture and clinical validation of its features across 12 diseases. We further demonstrate the application of BlaBla to a task visualizing and classifying language disorders in three languages on real clinical data from the AphasiaBank dataset. We make the codebase freely available to researchers with the hope of providing a consistent, well-validated foundation for the next generation of clinical linguistic research.
Early Linguistic Pattern of Anxiety from Social Media Using Interpretable Linguistic Features: A Multi-Faceted Validation Study with Author-Disjoint Evaluation
FDA drug labels are rich sources of information about drugs and drug-disease relations, but their complexity makes them challenging texts to analyze in isolation. To overcome this, we situate these labels in two health knowledge graphs: one built from precise structured information about drugs and diseases, and another built entirely from a database of clinical narrative texts using simple heuristic methods. We show that Probabilistic Soft Logic models defined over these graphs are superior to text-only and relation-only variants, and that the clinical narratives graph delivers exceptional res…
Cross-Linguistic Persona-Driven Data Synthesis for Robust Multimodal Cognitive Decline Detection
Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Informatics, Nanjing Medical University（智能诊疗技术与仪器工程研究中心、教育部、生物医学工程与信息学院、南京医科大学）；Institute of Biomedical Engineering, University of Oxford（生物医学工程研究所、牛津大学）；Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR)（高性能计算研究所、科技研究局（A*STAR））；Department of Neurology, National Neuroscience Institute, Singapore, 308433, Singapore（神经病学部、新加坡国家神经科学研究所）；Duke-NUS Medical School, Singapore, 169857, Singapore（新加坡国立大学医学院）
Cross-Lingual Mental Health Ontologies for Indian Languages: Bridging Patient Expression and Clinical Understanding through Explainable AI and Human-in-the-Loop Validation
University of Florida（佛罗里达大学）；University of Kansas（堪萨斯大学）；University of Maryland（马里兰大学）；IIT Kharagpur（印度理工学院卡拉格普尔分校）
BITS Pilani, K.K. Birla Goa Campus（Birla技术与科学学院皮拉尼校区 K.K. Birla果阿校区）；IEMN Université de Lille（里尔大学微电子与纳米技术研究所）
Alzheimers disease is a fatal progressive brain disorder that worsens with time. It is high time we have inexpensive and quick clinical diagnostic techniques for early detection and care. In previous studies, various Machine Learning techniques and Pre-trained Deep Learning models have been used in conjunction with the extraction of various acoustic and linguistic features. Our study focuses on three models for the classification task in the ADReSS (The Alzheimers Dementia Recognition through Spontaneous Speech) 2021 Challenge. We use the well-balanced dataset provided by the ADReSS Challenge …
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes
College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校信息与计算机科学学院）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
National Technical University of Athens（雅典国立技术大学）；Behavioral Signal Technologies（行为信号技术公司）
In this work we propose a machine learning model for depression detection from transcribed clinical interviews. Depression is a mental disorder that impacts not only the subject's mood but also the use of language. To this end we use a Hierarchical Attention Network to classify interviews of depressed subjects. We augment the attention layer of our model with a conditioning mechanism on linguistic features, extracted from affective lexica. Our analysis shows that individuals diagnosed with depression use affective language to a greater extent than not-depressed. Our experiments show that exter…
Winterlight Labs（Winterlight实验室）；University of Toronto（多伦多大学）；Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto ON, Canada（李卡廷知识研究所，圣迈克尔医院）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Surgical Safety Technologies（外科安全技术公司（注：此处原文本未明确该机构性质，但根据常见命名推测为公司类机构，符合提取范围））
Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data can be expensive, and hand-crafting features is burdensome. In this paper, we take a third approach, proposing Consensus Networks (CNs), a framework to classify after reaching agreements between modalities. We divide linguistic features into non-overlapping subsets according to their modalities, and let neural networks…
Alzheimer`s disease (AD)-related global healthcare cost is estimated to be $1 trillion by 2050. Currently, there is no cure for this disease; however, clinical studies show that early diagnosis and intervention helps to extend the quality of life and inform technologies for personalized mental healthcare. Clinical research indicates that the onset and progression of Alzheimer`s disease lead to dementia and other mental health issues. As a result, the language capabilities of patient start to decline. In this paper, we show that machine learning-based unsupervised clustering of and anomaly dete…
Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）；Imperial College Healthcare NHS Trust（帝国理工学院医疗保健国民保健服务信托基金）；University of Surrey（萨里大学）；University of Southern California（南加州大学）
The grip of grammar on meaning uncertainty: cross-linguistic evidence, neural correlates, and clinical relevance
Grammar and Cognition Lab, Department of Translation & Language Sciences, Universitat Pompeu Fabra（语法与认知实验室，翻译与语言科学系，庞培法布拉大学）；School of Foreign Studies, Guangzhou University（外语学院，广州大学）；Department of Neurology Sant Pau Memory Unit, Hospital de la Santa Creu i Sant Pau Biomedical Research Institute Sant Pau（圣帕乌记忆单位神经科，圣帕乌生物医学研究机构）；Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School（前额叶疾病单位，神经科，马萨诸塞总医院，哈佛医学院）；Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya（巴塞罗那阿尔茨海默病中心，国际加泰罗尼亚大学）；Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III（神经退行性疾病网络研究机构（CIBERNED），卡洛斯三世健康研究所）；Department of Language Science and Technology, Saarland University（语言科学与技术系，萨尔兰大学）；Department of Operation and Management, Zhuhai People's Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University)（珠海人民医院（北京理工大学附属珠海临床医学院））；Department of Statistics, Zhuhai People's Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University)（珠海人民医院（北京理工大学附属珠海临床医学院））；Faculty of Medicine, Macau University of Science and Technology（医学学院，澳门科技大学）；CIDCL, Escuela de Fonoaudiología, Universidad de Valparaíso（CIDCL，语言治疗学系，瓦尔帕莱索大学）；Department of Psychiatry and Psychotherapy, University of Marburg（精神病学与心理学系，马尔堡大学）；Marburg University, School of Medicine, Department of Psychiatry and Psychotherapy（马尔堡大学，医学院，精神病学与心理学系）；Department of Adult Psychiatry and Psychotherapy, University of Zurich（成人精神病学与心理学系，苏黎世大学）；Neuroscience Center Zurich, University of Zurich and ETH Zurich（苏黎世神经科学中心，苏黎世大学和ETH苏黎世联邦理工学院）；Center for Clinical Neuroscience and Cognition and Department of Psychiatry, University of Groningen, University Medical Center Groningen（临床神经科学与认知中心及精神病学系，格罗宁根大学，格罗宁根大学医学中心）；Douglas Mental Health University Institute, Department of Psychiatry, McGill University（道格拉斯大学心理健康研究所，精神病学系，麦吉尔大学）；Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University（医学生物物理系，施吕赫尔医学院及牙科学院，西部大学）；Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University（罗巴茨研究所，施吕赫尔医学院及牙科学院，西部大学）；Institut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain（加泰罗尼亚高级研究与研究机构（ICREA），巴塞罗那，西班牙）
Component-Level Lesioning of Language Models Reveals Clinically Aligned Aphasia Phenotypes
Department of Computer Science, The University of Manchester, UK（曼彻斯特大学计算机科学系）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China（多模态人工智能系统国家重点实验室，中国科学院自动化研究所，北京）；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China（中国科学院大学人工智能学院，北京）；Department of Linguistics and Translation, City University of Hong Kong, Hong Kong（香港城市大学语言学与翻译系）；Department of Language Science and Technology, Hong Kong Polytechnic University, Hong Kong（香港理工大学语言科学与技术系）
Computational analysis of the language of pain: a systematic review
Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento（系统与计算机工程研究与开发研究所）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Instituto de Investigação e Inovação em Saúde da Universidade do Porto (I3S)（波尔图大学健康研究与创新研究所）；Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto（波尔图大学分子与细胞生物学研究所）；Faculdade de Medicina, Universidade do Porto（波尔图大学医学院）
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time
MedGUIDE: Benchmarking Clinical Decision-Making in Large Language Models
Harvard University（哈佛大学）；MIT（麻省理工学院）；Cornell University（康奈尔大学）；Mayo Clinic（梅奥诊所）；UIUC（伊利诺伊大学厄巴纳-香槟分校）；University of Virginia（弗吉尼亚大学）；Harvard Medical School（哈佛医学院）；Abaka AI；NYU（纽约大学）
HwaMei Hospital, University of Chinese Academy of Sciences（中国科学院大学华美医院）；Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences（中国科学院大学宁波生命与健康产业研究院）
Medical information extraction consists of a group of natural language processing (NLP) tasks, which collaboratively convert clinical text to pre-defined structured formats. Current state-of-the-art (SOTA) NLP models are highly integrated with deep learning techniques and thus require massive annotated linguistic data. This study presents an engineering framework of medical entity recognition, relation extraction and attribute extraction, which are unified in annotation, modeling and evaluation. Specifically, the annotation scheme is comprehensive, and compatible between tasks, especially for …
Algorithmic Reflectivity in Large Language Models: Methodological Study of Linguistic Structure in 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）
LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data
Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）；University of Tehran（德黑兰大学）；Columbia University（哥伦比亚大学）；University of Florida（佛罗里达大学）
TCM-3CEval: A Triaxial Benchmark for Assessing Responses from Large Language Models in Traditional Chinese Medicine
School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine（上海中医药大学针灸推拿学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；China Academy of Chinese Medical Sciences（中国中医科学院）
GS-BrainText: A Multi-Site Brain Imaging Report Dataset from Generation Scotland for Clinical Natural Language Processing Development and Validation
Advanced Care Research Centre, University of Edinburgh（爱丁堡大学高级护理研究中心）；School of Literatures, Languages and Cultures, University of Edinburgh（爱丁堡大学文学、语言与文化学院）；Edinburgh Futures Institute, University of Edinburgh（爱丁堡大学爱丁堡未来研究所）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Usher Institute, University of Edinburgh（爱丁堡大学厄舍研究所）；Institute for Neuroscience and Cardiovascular Research, University of Edinburgh（爱丁堡大学神经科学与心血管研究所）；Generation Scotland, Institute of Genetics and Cancer, University of Edinburgh（爱丁堡大学遗传学与癌症研究所苏格兰世代研究中心）；NHS Lothian（NHS洛锡安）；School of Mathematical and Computer Sciences, Heriot-Watt University（赫瑞瓦特大学数学与计算机科学学院）
Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Universidade da Coruña（拉科鲁尼亚大学）
It is often stated that human languages, as other biological systems, are shaped by cost-cutting pressures but, to what extent? Attempts to quantify the degree of optimality of languages by means of an optimality score have been scarce and focused mostly on English. Here we recast the problem of the optimality of the word order of a sentence as an optimization problem on a spatial network where the vertices are words, arcs indicate syntactic dependencies and the space is defined by the linear order of the words in the sentence. We introduce a new score to quantify the cognitive pressure to red…
A Study of Generative Large Language Model for Medical Research and Healthcare
University of Florida（佛罗里达大学）；University of Florida Health Cancer Center（佛罗里达大学健康癌症中心）；NVIDIA（英伟达）；UF Clinical and Translational Science Institute（佛罗里达大学临床与转化科学研究所）
Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs
Department of Computer Science and Engineering, College of Engineering, Qatar University（卡塔尔大学计算机科学与工程系）；College of Science and Engineering, Hamad Bin Khalifa University (HBKU)（哈马德·本·卡伊夫大学（HBKU）理学院）；Primary Health Care Corporation (PHCC)（初级卫生保健公司）；Birmingham City University（伯明翰城市大学）
ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis
Zhejiang University School of Medicine（浙江大学医学院）；The First Affiliated Hospital（第一附属医院）；Liangzhu Laboratory（良渚实验室）；Shandong Jianzhu University（山东建筑大学）；FUXI AI Lab（伏羲人工智能实验室）；Beijing Normal University（北京师范大学）；Southern University of Science and Technology（南方科技大学）；China University of Geosciences (Wuhan)（中国地质大学（武汉））；Hangzhou Baorun Biotechnology Co., Ltd.（杭州宝润生物科技有限公司）
A Methodology for Explainable Large Language Models with Integrated Gradients and Linguistic Analysis in Text Classification
Bioinformatics Multidisciplinary Environment (BioME)（生物信息学多学科环境（BioME））；Digital Metropolis Institute (IMD)（数字大都市研究所（IMD））；Federal University of Rio Grande do Norte (UFRN)（北里奥格兰德联邦大学（UFRN））；Mobile Brain（移动大脑（公司））；Institute of Psychiatry (IPUB)（精神病学研究所（IPUB））；Federal University of Rio de Janeiro (UFRJ)（里约热内卢联邦大学（UFRJ））；The University of Exeter（埃克塞特大学）；Institute for Data Science and Artificial Intelligence at the University of Exeter（埃克塞特大学数据科学与人工智能研究所）；The University of Sheffield（谢菲尔德大学）；Pontifical Catholic University of Rio Grande do Sul (PUCRS)（南里奥格兰德天主教大学（PUCRS））
Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models
MedVisAI Lab, Singapore（新加坡MedVisAI实验室）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China（中国上海交通大学医学院瑞金医院）；Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore（新加坡南洋理工大学Lee Kong Chian医学院）
