Applying methods in natural language processing on electronic health records (EHR) data is a growing field. Existing corpus and annotation focus on modeling textual features and relation prediction. However, there is a paucity of annotated corpus built to model clinical diagnostic thinking, a process involving text understanding, domain knowledge abstraction and reasoning. This work introduces a hierarchical annotation schema with three stages to address clinical text understanding, clinical reasoning, and summarization. We created an annotated corpus based on an extensive collection of publicly available daily progress notes, a type of EHR documentation that is collected in time series in a problem-oriented format. The conventional format for a progress note follows a Subjective, Objective, Assessment and Plan heading (SOAP). We also define a new suite of tasks, Progress Note Understanding, with three tasks utilizing the three annotation stages. The novel suite of tasks was designed to train and evaluate future NLP models for clinical text understanding, clinical knowledge representation, inference, and summarization.
Cross-Lingual Clinical Annotation Projection as Constrained Text Generation: A Six-Language Study
Universidad de Málaga（马拉加大学）；Research Institute of Multilingual Language Technologies, Universidad de Málaga（马拉加大学多语言语言技术研究所）；IBIMA Plataforma BIONAND, Instituto de Investigación Biomédica de Málaga（IBIMA BIONAND平台，马拉加生物医学研究所）
Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit
King’s College London（伦敦国王学院）；University College London（伦敦大学学院）；Health Data Research UK（英国健康数据研究所）；King’s College Hospital NHS Foundation Trust（国王学院医院国民保健服务基金会信托）
电子健康记录（EHR）包含大量非结构化文本，需要应用信息抽取（IE）技术来实现临床分析。我们提出了开源医学概念注释工具包（MedCAT），它提供：a）一种新的自监督机器学习算法，用于使用包括UMLS/SNOMED-CT在内的任何概念词汇提取概念; b）一个功能丰富的注释接口，用于定制和训练IE模型; c）集成到更广泛的CogStack生态系统中，用于与供应商无关的健康系统部署。我们在从开放数据集中提取UMLS概念方面表现出了改进的性能（F1：0.448-0.738 vs 0.429-0.650）。进一步的真实世界验证证明了伦敦3家大型医院的SNOMED-CT提取，其中自监督训练来自约1700万临床记录的约8.8B单词，并使用约6 K临床医生注释的示例进行进一步微调。我们在医院、数据集和概念类型之间显示出很强的可移植性（F1 > 0.94），这表明跨领域EHR不可知的实用性，可用于加速临床和研究用例。
Synthesized Annotation Guidelines are Knowledge-Lite Boosters for Clinical Information Extraction
McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦克威廉姆斯生物医学信息学院）；School of Dentistry, University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心牙科学院）；Enterprise Development and Integration, University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心企业开发与集成部）；Virginia Tech Carilion School of Medicine, Department of Health Systems & Implementation Science（弗吉尼亚理工大学卡里利恩医学院卫生系统与实施科学系）
NUBES: A Corpus of Negation and Uncertainty in Spanish Clinical Texts
Vicomtech Foundation（维孔特克基金会）；Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟（BRTA））；HiTZ centre（HiTZ中心）；University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））
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）
PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs
Department of Biostatistics, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校生物统计学系）；Department of Genetics, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校遗传学系）；Curriculum for Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校生物信息学与计算生物学课程）；Carolina Health Informatics Program, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校健康信息学计划）；Department of Statistics and Operations Research, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校统计学与运筹学系）；Department of Otolaryngology/Head and Neck Surgery, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校耳鼻喉科及头颈外科系）；Department of Statistics, University of Michigan（密歇根大学统计学系）；Department of Biomedical Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校阿德姆牙科学院生物医学科学系）；Computational Medicine Program, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校计算医学计划）；Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校林伯格综合癌症中心）
A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models
Oak Ridge National Laboratory（橡树岭国家实验室）；Program Evaluation and Resource Center, Office of Mental Health and Office of Suicide Prevention, Department of Veterans Affairs（退伍军人事务部心理健康办公室和自杀预防办公室项目评估与资源中心）；Vanderbilt University Medical Center（范德比尔特大学医学中心）；VA Maryland Health Care System（退伍军人事务部马里兰医疗保健系统）；VA Desert Pacific Healthcare Network（退伍军人事务部沙漠太平洋医疗网络）；VA Connecticut Health Care System（退伍军人事务部康涅狄格医疗保健系统）；Yale School of Medicine（耶鲁医学院）
Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis
Machine Learning Department and Heinz College Carnegie Mellon University（卡内基梅隆大学机器学习系和海恩兹学院）；National Library of Medicine National Institutes of Health（美国国家医学图书馆）
IBM Almaden Research Center（IBM阿尔马登研究中心）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）；MIT Critical Data（麻省理工学院关键数据实验室）；Albert Einstein Healthcare Network-Philadelphia Campus（阿尔伯特·爱因斯坦医疗网络费城院区）；Harvard Medical School（哈佛医学院）
CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation
Center for Artificial Intelligence in Medicine and Imaging（医学与影像人工智能中心）；Department of Radiology（放射科）；Department of Pediatrics（儿科）；Department of Radiology, Weill Cornell Medicine（韦氏 Cornell 医学院放射科）；Department of Radiology, Brigham and Women’s Hospital（布里奇妇产科医院放射科）；University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）；Department of Biomedical Data Science（生物医学数据科学部）
Lung Cancer Concept Annotation from Spanish Clinical Narratives
Centro de Tecnología Biomédica, Universidad Politécnica de Madrid（马德里理工大学生物医学技术中心）；Universidad Politécnica de Madrid（马德里理工大学）；ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid（马德里理工大学计算机工程高等技术学院）
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Ricoh Software Research Center (Beijing)（理光软件研究所（北京））；School of Software, Harbin University of Science and Technology（哈尔滨理工大学软件学院）；Medical Records Room, the Second Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第二医院病案室）
Automated SNOMED CT Concept Annotation in Clinical Text Using Bi-GRU Neural Networks
Informatics and Analytics, University of North Carolina Greensboro（信息学与分析，北卡罗来纳大学格林斯堡分校）；Department of Computer Science, University of Nebraska Omaha（计算机科学系，内布拉斯加大学奥马哈分校）
A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text
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）
Use of 'off-the-shelf' information extraction algorithms in clinical informatics: a feasibility study of MetaMap annotation of Italian medical notes
Consiglio Nazionale delle Ricerche (CNR)（意大利国家研究委员会）；Politecnico di Milano（米兰理工大学）；Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Milano（米兰大医院基金会）
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition
School of Information Science and Engineering, East China University of Science and Technology（华东理工大学信息科学与工程学院）；Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education（化工过程先进控制和优化技术教育部重点实验室）
临床命名实体识别（Clinical Named Entity Recognition，CNER）的目的是自动识别电子健康档案（Electronic Health Records，EHR）中的临床术语，这是临床研究的基础和关键步骤。为CNER训练高性能模型，通常需要大量具有高质量标签的EHR。然而，给电子病历，尤其是中文版的电子病历贴上标签既费时又费钱。对此的一个有效解决方案是主动学习，其中模型要求标注器注释模型不确定的数据。传统的主动学习假设一个单一的标签，总是回答无声的答案查询标签。然而，在实际设置中，多个标注器以不同的成本提供不同质量的标注，并且总体标注质量较低的标注器仍然可以为某些特定实例分配正确的标签。在本文中，我们提出了一个成本-质量自适应主动学习（CQAAL）的方法CNER在中国电子病历，它保持了注释质量之间的平衡，标签成本，和所选实例的信息量。具体来说，CQAAL选择具有成本效益的实例-标签对，以自适应的方式以较低的成本实现更好的注释质量。CCKS-2017任务2基准数据集上的计算结果证明了所提出的CQAAL的优越性和有效性。
DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing
University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Loyola University Chicago（芝加哥洛约拉大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard University（哈佛大学）；Rush University Medical Center（拉什大学医学中心）
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations
The University of Sydney（悉尼大学）；Northeastern University（东北大学）；IHPC, A*STAR（新加坡科技研究局高性能计算研究所）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Harvard Medical School（哈佛医学院）
