National NLP Clinical Challenges (n2c2): Challenges in Natural Language Processing for Clinical Narratives

国家 NLP 临床挑战 (n2c2)：临床叙述自然语言处理的挑战

• 批准号: 10393499
• 负责人: Ozlem Uzuner
• 金额: $ 2万
• 依托单位: GEORGE MASON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10393499
• 关键词: Access to Information; Address; American; Clinic; Clinical; Collaborations; Communities; Community Developments; Complement; Data; Data Science; Data Set; Development; Educational workshop; Electronic Health Record; Evaluation; Fostering; Future; Future Generations; Goals; Gold; Grant; Growth; Hand; Head; Healthcare; Improve Access; Individual; Informatics; Institution; Israel; Journals; Knowledge; Measures; Medical Informatics; Medical center; Methodology; Natural Language Processing; Outcome; Paper; Peer Review; Performance; Privacy; Publications; Publishing; Records; Research; Research Personnel; Rest; Running; Series; Source; Structure; System; Systems Development; Targeted Research; Technology; Time; United States National Institutes of Health; United States National Library of Medicine; Universities; base; biomedical informatics; clinical development; computerized; falls; head-to-head comparison; health data; indexing; medical schools; meetings; practical application; programs; symposium; usability; working group

Project Summary and Abstract Narratives of electronic health records (EHRs) contain useful information that is difficult to automatically extract, index, search, or interpret. Natural language processing (NLP) technologies can extract this information and convert it in to a structured format that is more readily accessible by computerized systems. However, the development of NLP systems is contingent on access to relevant data and EHRs are notoriously difficult to obtain because of privacy reasons. Despite the recent efforts to de-identify and release narrative EHRs for research, these data are still very rare. As a result, clinical NLP, as a field has lagged behind. To address this problem, since 2006, we organized thirteen shared tasks, accompanied with workshops and journal publications. Twelve of these shared tasks have focused on the development of clinical NLP systems and the remaining one on the usability of these systems. We have covered both depth and breadth in terms of shared tasks, preparing tasks that study cutting-edge NLP problems on a variety of EHR data from multiple institutions. Our shared tasks are the longest running series of clinical NLP shared tasks, with ever growing EHR data sets, tasks, and participation. Our most popular three data sets have been cited 495 (2010 data), 284 (2006 de-id data), and 274 (2009 data) times, respectively, representing hundreds of articles that have come out of these three data sets alone. Our goal in this proposal is to continue the efforts we started in 2006 under i2b2 shared task challenges (i2b2, NIH NLM U54LM008748, PI: Kohane and R13 LM011411, PI: Uzuner) to de-identify EHRs, annotate them with gold- standard annotations for clinical NLP tasks, and release them to the research community for the development and head-to-head comparison of clinical NLP systems, for the advancement of the state of the art. Continuing our efforts under National NLP Clinical Challenges (n2c2) based at the Health Data Science program of the newly established Department of Biomedical Informatics at Harvard Medical School, we aim to form partnerships with the community to grow the shared task efforts in several ways: (1) grow the available de-identified EHR data sets through partnerships that can contribute to the volume and variety of the data, and (2) grow the available gold-standard annotations in terms of depth and breadth of NLP tasks. Given these aims and partnerships, we plan to hold a series of shared tasks. We will complement these shared tasks with workshops that meet in conjunction with the Fall Symposium of the American Medical Informatics Association and with journal special issues so that advancement of the state of the art can be sped up and future generations can build on the past.

项目摘要和摘要 电子健康记录（EHR）的叙述包含的有用信息很难自动提取， 索引，搜索或解释。自然语言处理（NLP）技术可以提取此信息，并且 将其转换为结构化格式，该格式可以通过计算机系统更容易访问。但是， NLP系统的开发取决于访问相关数据，众所周知，EHR很难获得 由于隐私原因。尽管最近努力识别和释放叙事EHR进行研究，但 这些数据仍然非常罕见。结果，临床NLP，因为一个领域落后了。为了解决这个问题， 自2006年以来，我们组织了13项分享任务，并伴随着讲习班和期刊出版物。十二 这些共同任务的重点是临床NLP系统的发展，其余的任务是 这些系统的可用性。我们已经在共同的任务上涵盖了深度和广度，准备任务 研究来自多个机构的各种EHR数据的尖端NLP问题。我们共同的任务是 运行时间最长的一系列临床NLP共享任务，EHR数据集，任务和参与不断增长。 我们最受欢迎的三个数据集被引用了495（2010年数据），284（2006 DE-ID数据）和274（2009年数据） 时代分别代表了这三个数据集中出现的数百篇文章。我们的 该提案中的目标是继续我们2006年在I2B2共享任务挑战下开始的努力（I2B2，NIH NLM U54LM008748，PI：KOHANE和R13 LM011411，PI：UZUNER），以识别EHRS，用Gold-注释它们 临床NLP任务的标准注释，并将其释放到研究社区以进行开发 以及临床NLP系统的正面比较，以提高最新状态。继续 我们在国家NLP临床挑战（N2C2）下的努力（N2C2） 新成立的生物医学信息学系在哈佛医学院，我们的目标是建立伙伴关系 随着社区以多种方式发展共同的任务工作：（1）增长可用的识别EHR数据 通过可以为数据的数量和种类做出贡献的合作伙伴关系，（2）增长可用的 金标准的注释在NLP任务的深度和广度方面。鉴于这些目标和伙伴关系，我们 计划执行一系列共享任务。我们将通过在其中相遇的研讨会来补充这些共同的任务 与美国医学信息学协会的秋季研讨会和杂志特别节目结合 问题，以便可以加快艺术状态的进步，而后代可以建立在过去。