Clinical notes are unstructured text generated by clinicians during patient encounters. Clinical notes are usually accompanied by a set of metadata codes from the International Classification of Diseases(ICD). ICD code is an important code used in various operations, including insurance, reimbursement, medical diagnosis, etc. Therefore, it is important to classify ICD codes quickly and accurately. However, annotating these codes is costly and time-consuming. So we propose a model based on bidirectional encoder representations from transformers (BERT) using the sequence attention method for automatic ICD code assignment. We evaluate our approach on the medical information mart for intensive care III (MIMIC-III) benchmark dataset. Our model achieved performance of macro-averaged F1: 0.62898 and micro-averaged F1: 0.68555 and is performing better than a performance of the state-of-the-art model using the MIMIC-III dataset. The contribution of this study proposes a method of using BERT that can be applied to documents and a sequence attention method that can capture important sequence in-formation appearing in documents.
MIMIC-\RNum{4}-Ext-22MCTS: A 22 Millions-Event Temporal Clinical Time-Series Dataset with Relative Timestamp for Risk Prediction
National Library of Medicine（国家医学图书馆）；AWS AI Labs（AWS人工智能实验室）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Stevens Institute of Technology（史蒂文斯理工学院）；Carnegie Mellon University（卡内基梅隆大学）
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）
Trajectory-guided discharge stratification for heart failure using short-context electronic health record sequence modeling
Sahlgrenska University Hospital（斯德哥尔摩大学医院）；Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg（计算机科学与工程系，查尔姆斯理工大学和乌普萨拉大学）；Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg（分子与临床医学系，斯德哥尔摩学院，乌普萨拉大学）；Department of Medicine, Geriatrics and Emergency Medicine, Sahlgrenska University Hospital（医学、老年医学和急诊医学系，斯德哥尔摩大学医院）；Department of Food and Nutrition, and Sport Science, Faculty of Education, University of Gothenburg（营养学与体育科学系，教育学院，乌普萨拉大学）；School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg（公共卫生与社区医学系，医学院，乌普萨拉大学）
A BERT-Style Self-Supervised Learning CNN for Disease Identification from Retinal Images
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）；McKeley School of Engineering, Washington University in St. Louis（工程学院，华盛顿大学圣路易斯分校）；Department of Neurology, Mayo Clinic（神经病学部，梅奥诊所）
Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke
NYU Langone Health（纽约大学朗格尼医学中心）；New York University（纽约大学）；Washington University in Saint Louis（圣路易斯华盛顿大学）；Columbia University Vagelos College of Physicians and Surgeons（哥伦比亚大学瓦格洛斯内外科医学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Kyung Hee University（庆熙大学）
