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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要和摘要电子健康记录 (EHR) 的叙述包含难以自动提取的有用信息，索引、搜索或解释。自然语言处理（NLP）技术可以提取这些信息并将其转换为计算机系统更容易访问的结构化格式。然而， NLP 系统的开发取决于相关数据的获取，而 EHR 的获取是出了名的困难由于隐私原因。尽管最近努力去识别和发布叙述性电子病历以供研究，这些数据仍然非常罕见。结果，临床 NLP 作为一个领域已经落后了。为了解决这个问题，自 2006 年以来，我们组织了 13 项共同任务，并举办了研讨会和期刊出版物。十二这些共享任务中的一部分集中在临床 NLP 系统的开发上，其余的则集中在临床 NLP 系统的开发上。这些系统的可用性。我们在共享任务、准备任务方面涵盖了深度和广度研究来自多个机构的各种 EHR 数据的前沿 NLP 问题。我们的共同任务是运行时间最长的临床 NLP 共享任务系列，其 EHR 数据集、任务和参与度不断增长。我们最受欢迎的三个数据集被引用次数为495（2010年数据）、284（2006年de-id数据）和274（2009年数据）次，分别代表仅来自这三个数据集的数百篇文章。我们的本提案的目标是继续我们 2006 年在 i2b2 共享任务挑战（i2b2、NIH NLM U54LM008748，PI：Kohane 和 R13 LM011411，PI：Uzuner）对 EHR 进行去识别化，用金色对其进行注释- 临床NLP任务的标准注释，并发布给研究社区开发以及临床 NLP 系统的头对头比较，以推进最先进的技术。继续我们在国家 NLP 临床挑战 (n2c2) 下的努力基于健康数据科学计划哈佛医学院新成立生物医学信息学系，我们旨在建立合作伙伴关系与社区合作，通过多种方式扩大共享任务的力度：(1) 扩大可用的去识别化 EHR 数据通过合作伙伴关系来增加数据的数量和种类，以及 (2) 增加可用数据 NLP 任务的深度和广度方面的黄金标准注释。鉴于这些目标和伙伴关系，我们计划举行一系列共同任务。我们将通过举办研讨会来补充这些共同任务与美国医学信息学协会秋季研讨会以及期刊特别会议相结合问题，以便加快最先进技术的进步，让后代能够在过去的基础上再接再厉。