PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data
Department Of Emergency Medicine , Yale School of Medicine , 464 Congress Ave , 06519 , CT , USA；Department of Biomedical Informatics \& Data Science , Yale School of Medicine , 100 College Street , 06510 , CT , USA；Department of Social；Behavioral Sciences , Yale School of Public Health , 60 College Street , 06520 , CT , USA；Division of Research , Texas State University , 601 University Dr. , 78666 , TX , USA
Samah Fodeh, Linhai Ma, Yan Wang, Srivani Talakokkul, Ganesh Puthiaraju, Afshan Khan, Ashley Hagaman, Sarah Lowe, Aimee Roundtree
Patient-generated text such as secure messages, surveys, and interviews contains rich expressions of the patient voice (PV), reflecting communicative behaviors and social determinants of health (SDoH). Traditional qualitative coding frameworks are labor intensive and do not scale to large volumes of patient-authored messages across health systems. Existing machine learning (ML) and natural language processing (NLP) approaches provide partial solutions but often treat patient-centered communication (PCC) and SDoH as separate tasks or rely on models not well suited to patient-facing language. We introduce PVminer, a domain-adapted NLP framework for structuring patient voice in secure patient-provider communication. PVminer formulates PV detection as a multi-label, multi-class prediction task integrating patient-specific BERT encoders (PV-BERT-base and PV-BERT-large), unsupervised topic modeling for thematic augmentation (PV-Topic-BERT), and fine-tuned classifiers for Code, Subcode, and Combo-level labels. Topic representations are incorporated during fine-tuning and inference to enrich semantic inputs. PVminer achieves strong performance across hierarchical tasks and outperforms biomedical and clinical pre-trained baselines, achieving F1 scores of 82.25% (Code), 80.14% (Subcode), and up to 77.87% (Combo). An ablation study further shows that author identity and topic-based augmentation each contribute meaningful gains. Pre-trained models, source code, and documentation will be publicly released, with annotated datasets available upon request for research use.
Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations
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）
Analyzing Cancer Patients' Experiences with Embedding-based Topic Modeling and LLMs
Leiden Institute of Advanced Computer Science (LIACS), Leiden, The Netherlands（莱顿高级计算机科学研究所（LIACS），莱顿，荷兰）；Leiden University Medical Center (LUMC), Leiden, The Netherlands（莱顿大学医学中心（LUMC），莱顿，荷兰）
CBT-Audio: Evaluating Audio Language Models for Patient-Side Distress Intensity Estimation in CBT Session Recordings
School of Computer Science, Faculty of Engineering, University of Sydney, Australia（悉尼大学工程学院计算机科学学院，澳大利亚）；School of Psychology, Faculty of Science, University of Sydney, Australia（悉尼大学科学学院心理学学院，澳大利亚）；School of Computing, Faculty of Science and Engineering, Macquarie University, Australia（麦考瑞大学科学与工程学院计算学院，澳大利亚）；CHeBA (Centre for Healthy Brain Ageing), School of Clinical Medicine, Discipline of Psychiatry & Mental Health, The University of New South Wales, Australia（新南威尔士大学临床医学学院精神病与心理健康学科健康大脑年龄中心，澳大利亚）；Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Australia（悉尼大学医学与健康学院公共卫生学院，澳大利亚）
SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment
National Research Council of Italy (CNR), Institute for High-Performance Computing and Networking (ICAR), Naples（意大利国家研究理事会（CNR）、高性能计算与网络研究所（ICAR）、那不勒斯）
Voice-Interactive Surgical Agent for Multimodal Patient Data Control
Image Guided Precision Cancer Surgery Institute, College of Medicine, Korea University（影像引导精准癌症手术研究所，韩国大学医学院）；Department of Radiology, Massachusetts General Hospital（放射科，麻省总医院）；Department of Thoracic and Cardiovascular Surgery, Korea University Guro Hospital, College of Medicine, Korea University（胸外科和心血管外科，韩国大学Guro医院，韩国大学医学院）；Department of Biomedical Sciences, College of Medicine, Korea University（生物医学科学系，韩国大学医学院）
Multimodal Laryngoscopic Video Analysis for Assisted Diagnosis of Vocal Fold Paralysis
School of Computer Science（计算机科学学院）；Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems（多模态智能系统苏州市重点实验室）；Sun Yat-sen Memorial Hospital of Sun Yat-sen University（中山大学孙逸仙纪念医院）；School of Artificial Intelligent（人工智能学院）；Department of Otorhinolaryngology（耳鼻喉科部门）
AmarDoctor: An AI-Driven, Multilingual, Voice-Interactive Digital Health Application for Primary Care Triage and Patient Management to Bridge the Digital Health Divide for Bengali Speakers
MedAi Bangladesh Limited（MedAi孟加拉有限公司）；Central Police Hospital, Dhaka, Bangladesh（达卡警察医院）；University of Birmingham, UK（英国伯明翰大学）
Seoul National University（首尔大学）；Seoul National University Bundang Hospital（首尔大学盆唐医院）；Seoul National University College of Medicine（首尔大学医学院）
In this study, we present a speech corpus of patients with chronic kidney disease (CKD) that will be used for research on pathological voice analysis, automatic illness identification, and severity prediction. This paper introduces the steps involved in creating this corpus, including the choice of speech-related parameters and speech lists as well as the recording technique. The speakers in this corpus, 289 CKD patients with varying degrees of severity who were categorized based on estimated glomerular filtration rate (eGFR), delivered sustained vowels, sentence, and paragraph stimuli. This s…
Nagoya University（名古屋大学）；Academia Sinica（中央研究院）；Chi Mei Hospital（奇美医院）
We propose a new paradigm for maintaining speaker identity in dysarthric voice conversion (DVC). The poor quality of dysarthric speech can be greatly improved by statistical VC, but as the normal speech utterances of a dysarthria patient are nearly impossible to collect, previous work failed to recover the individuality of the patient. In light of this, we suggest a novel, two-stage approach for DVC, which is highly flexible in that no normal speech of the patient is required. First, a powerful parallel sequence-to-sequence model converts the input dysarthric speech into a normal speech of a r…
Towards Conversational Medical AI with Eyes, Ears and a Voice
Google DeepMind（谷歌DeepMind）；Google Research（谷歌研究）；Beth Israel Deaconess Medical Center, Harvard Medical School（贝塞斯达医院, 哈佛医学院）；Stanford University（斯坦福大学）
Belarusian State University of Informatics and Radioelectronics（白俄罗斯国立信息与无线电电子大学）；Republican Research and Clinical Center of Neurology and Neurosurgery（共和国神经学与神经外科研究临床中心）
Amyotrophic lateral sclerosis (ALS) is incurable neurological disorder with rapidly progressive course. Common early symptoms of ALS are difficulty in swallowing and speech. However, early acoustic manifestation of speech and voice symptoms is very variable, that making their detection very challenging, both by human specialists and automatic systems. This study presents an approach to voice assessment for automatic system that separates healthy people from patients with ALS. In particular, this work focus on analysing of sustain phonation of vowels /a/ and /i/ to perform automatic classificat…
Controllable Dysarthric Speech Synthesis with Patient-Specific Conditioning for Speaker-Diverse ASR Augmentation
Department of Language Science and Technology, The Hong Kong Polytechnic University（语言科学与技术系，香港理工大学）；College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine（康复医学学院，福建中医药大学）；Department of Imaging, Rehabilitation Hospital affiliated to Fujian University of Traditional Chinese Medicine（影像科，福建中医药大学附属康复医院）
A Chinese Heart Failure Status Speech Database with Universal and Personalised Classification
School of Information Science and Technology（信息科学与技术学院）；Advanced Computing and Storage Laboratory, 2012 Laboratories（先进计算与存储实验室）；Institute of High Performance Computing（高性能计算研究所）
Exploring Gender-Specific Speech Patterns in Automatic Suicide Risk Assessment
University of Augsburg（奥格斯堡大学）；TU Munich（慕尼黑工业大学）；District Hospital Augsburg（奥格斯堡地区医院）；German Center for Mental Health（德国心理健康中心）；Imperial College（帝国理工学院）
