Continuity of care is crucial to ensuring positive health outcomes for patients discharged from an inpatient hospital setting, and improved information sharing can help. To share information, caregivers write discharge notes containing action items to share with patients and their future caregivers, but these action items are easily lost due to the lengthiness of the documents. In this work, we describe our creation of a dataset of clinical action items annotated over MIMIC-III, the largest publicly available dataset of real clinical notes. This dataset, which we call CLIP, is annotated by physicians and covers 718 documents representing 100K sentences. We describe the task of extracting the action items from these documents as multi-aspect extractive summarization, with each aspect representing a type of action to be taken. We evaluate several machine learning models on this task, and show that the best models exploit in-domain language model pre-training on 59K unannotated documents, and incorporate context from neighboring sentences. We also propose an approach to pre-training data selection that allows us to explore the trade-off between size and domain-specificity of pre-training datasets for this task.
A longitudinal geospatial multimodal dataset of post-discharge frailty, physiology, mobility, and neighborhoods
University of Toronto（多伦多大学）；KITE Research Institute, Toronto Rehabilitation Institute, University Health Network（大学健康网络多伦多康复研究所KITE研究院）；American University of the Middle East（中东美国大学）
Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan
Tokyo Metropolitan University（东京都立大学）；RIKEN（理化学研究所）；National Hospital Organization（国立医院机构）；Kitami Institute of Technology（北见工业大学）
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
Massachusetts Institute of Technology（麻省理工学院）；University of Massachusetts（马萨诸塞大学）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Massachusetts General Hospital（麻省总医院）；Tufts University School of Medicine（塔夫茨大学医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Washington University School of Medicine（华盛顿大学医学院）；Philips Research North America（飞利浦北美研究院）；Harvard University（哈佛大学）；Adobe Research（Adobe研究院）
Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models
King’s College London（伦敦国王学院）；King’s College Hospital NHS Foundation Trust（国王学院医院NHS基金会信托）；University College London（伦敦大学学院）
De-identifying Australian Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models
University of New South Wales（新南威尔士大学）；The Australian e-Health Research Centre, CSIRO（澳大利亚联邦科学与工业研究组织澳大利亚电子健康研究中心）；Australian Institute of Health and Welfare（澳大利亚卫生与福利研究所）
Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital
Department of Health Information Office, Data & Digitalization, University Medical Center（健康信息办公室、数据与数字化化大学医院）；Department of Trauma Surgery / Orthopedics, University Medical Center（创伤外科/骨科大学医院）；Department of Orthopaedic Surgery, Massachusetts General Hospital（骨科手术科马萨诸塞总医院）；Harvard Medical School Orthopaedic Trauma Initiative（哈佛医学院骨创伤倡议）；Department of Orthopaedic Surgery and Sports Medicine, Amsterdam UMC location University of Amsterdam（骨科手术与运动医学部门，阿姆斯特丹UMC地点大学）；Department of Orthopaedic Trauma, Flinders University Medical Centre（骨创伤部门，弗林德斯大学医疗中心）；Department of Otolaryngology – Head and Neck Surgery, University Medical Center（耳鼻喉科-头颈外科大学医院）；Department of Pediatrics, Pediatrics Nephrology, Beatrix Children’s Hospital, University Medical Center（儿科、儿科肾病学，比西特儿童医院，大学医院）
GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries
School of Computer Science, The University of Manchester（曼彻斯特大学计算机学院）；Health eResearch Center, The University of Manchester（曼彻斯特大学健康电子研究中心）；The Alan Turing Institute（艾伦·图灵研究所）
MIT（麻省理工学院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；IBM Research（IBM研究院）；Fidelity Investments（富达投资）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；University of the Philippines（菲律宾大学）；Northeastern University（东北大学）
Interpretable phenotyping of Heart Failure patients with Dutch discharge letters
MOX - Modelling and Scientific Computing Lab, Department of Mathematics, Politecnico di Milano, Milano, Italy（数学系模拟能力与科学计算实验室，米兰理工大学，意大利）；Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands（心脏病学部，阿姆斯特丹大学医学中心，阿姆斯特丹，荷兰）；Department of Medical Informatics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands（医学信息学部，阿姆斯特丹大学医学中心，阿姆斯特丹，荷兰）；Amsterdam Public Health, Digital Health, Personalized Medicine, Amsterdam, The Netherlands（阿姆斯特丹公共卫生，数字健康，个性化医学，荷兰阿姆斯特丹）；Amsterdam Cardiovascular Sciences, Atherosclerosis and Aortic Disease, Cardiomyopathy and Arrhythmia, Amsterdam, The Netherlands（阿姆斯特丹心血管科学，动脉粥样硬化和主动脉疾病，心肌病和心律失常，荷兰阿姆斯特丹）；Amsterdam Reproduction & Development, Amsterdam, The Netherlands（阿姆斯特丹生殖与发育，荷兰阿姆斯特丹）；Department of Epidemiology and Data Science, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands（流行病学与数据科学部，阿姆斯特丹大学医学中心，阿姆斯特丹，荷兰）；Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden（临床科学与教育部，索德斯医院，卡罗林斯卡研究所，斯德哥尔摩，瑞典）
Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine
University of Oxford（牛津大学）；Peking University（北京大学）；Yale University（耶鲁大学）；Tencent（腾讯）；Oxford-Suzhou Centre for Advanced Research（牛津-苏州高等研究院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）
DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits
Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Hyderabad Campus（计算机科学与信息系统系，比拉理工学院，比拉理工学院，海得拉巴校区）
EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries
KAIST（韩国科学技术院）；Seoul National University（首尔大学）；Seoul National University Bundang Hospital（首尔大学盆唐医院）；SAIHST, Sungkyunkwan University（成均馆大学）；Yonsei University College of Medicine（延世大学医学院）；Gangnam Severance Hospital（江南塞弗伦斯医院）；Severance Hospital（塞弗伦斯医院）；Seoul Medical Center（首尔医疗中心）；Seoul National University Hospital（首尔大学医院）；National Cancer Center（国立癌症中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Samsung Medical Center（三星医疗中心）
EHR-MCP: Real-world Evaluation of Clinical Information Retrieval by Large Language Models via Model Context Protocol
Department of Radiology, Keio University Hospital, Tokyo, Japan（Keio大学医院放射科）；Division of Infectious Diseases and Infection Control, Keio University Hospital, Tokyo, Japan（Keio大学医院感染病学与感染控制科）
