Challenges in Natural Language Processing for Clinical Narratives

临床叙述自然语言处理的挑战

• 批准号: 8913773
• 负责人: Ozlem Uzuner
• 金额: $ 1.98万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT ALBANY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8913773
• 关键词: Access to Information; Address; Agreement; Biology; Clinical; Clinical Research; Communities; Data; Data Set; Development; Distributed Systems; Educational workshop; Electronic Health Record; Evaluation; Future; Goals; Gold; Grant; Hand; Healthcare; Improve Access; Informatics; Institution; Journals; Knowledge; Learning; Medical Research; Natural Language Processing; Privacy; Public Health; Publications; Records; Research; Rest; Series; System; Systems Development; Targeted Research; Technology; Work; clinical application; data sharing; head-to-head comparison; improved; indexing; practical application; public health relevance; symposium

DESCRIPTION (provided by applicant): Narratives of electronic health records (EHRs) contain useful information that is difficult to automatically extract, index, search, or interpret. Clinical natural language processing (NLP) technologies for automatic extraction, indexing, searching, and interpretation of EHRs are in development; however, due to privacy concerns related to EHRs, such technologies are usually developed by teams that have privileged access to EHRs in a specific institution. Technologies that are tailored to a specific set of data from a given institution generate inspiring results on that data; however, they can fail to generalize to similar data from other institutions and even other departments from the same institution. Therefore, learning from these technologies and building on them becomes difficult. In order to improve NLP in EHRs, there is need for head-to-head comparison of approaches that can address a given task on the same data set. Shared-tasks provide one way of conducting systematic head-to- head comparisons. This proposal describes a series of shared-task challenges and conferences, spread over a five year period, that promote the development and evaluation of cutting edge clinical NLP systems by distributing de-identified EHRs to the broad research community, under data use agreements, so that: * the state-of-the-art in clinical NLP technologies can be identified and advanced, * a set of technologies that enable the use of the information contained in EHR narratives becomes available, and * the information from EHR narratives can be made more accessible, for example, for clinical and medical research. The scientific activities supporting the organization of the shared-task challenges are sponsored in part by Informatics for Integrating Biology and the Bedside (i2b2), grant number U54-LM008748, PI: Kohane. This proposal aims to organize a series of workshops, conference proceedings, and journal special issues that will accompany the shared-task challenges in order to disseminate the knowledge generated by the challenges.

描述（由申请人提供）：电子健康记录的叙述（EHR）包含有用的信息，这些信息很难自动提取，索引，搜索或解释。 用于自动提取，索引，搜索和解释EHR的临床自然语言处理（NLP）技术正在开发中；但是，由于与EHR有关的隐私问题，此类技术通常是由具有特定机构中EHR访问权限的团队开发的。根据给定机构量身定制的一组特定数据的技术会在该数据上产生鼓舞人心的结果；但是，他们可能无法概括来自其他机构甚至来自同一机构的其他部门的类似数据。因此，从这些技术中学习并在这些技术上学习变得困难。 为了改善EHR中的NLP，需要对可以在相同数据集上解决给定任务的方法进行正面比较。共享任务提供了一种进行系统的正面比较的方法。该提案描述了一系列在五年内分布的共享任务挑战和会议，这些挑战和会议通过在数据使用协议下向广泛的研究社区分配给广泛的研究社区，从而促进了尖端临床NLP系统的开发和评估，以： *临床NLP技术的最新技术可以被识别和先进， *一组能够使用EHR叙述中包含的信息的技术可用，并且 *例如，对于临床和医学研究，可以使EHR叙述的信息更容易获得。 支持共享任务挑战组织的科学活动部分是由整合生物学和床头（I2B2）的信息学赞助的（I2B2），赠款号U54-LM008748，PI：Kohane。 该提案旨在组织一系列的研讨会，会议记录和期刊特殊问题，这些问题将伴随共享任务挑战，以传播挑战所产生的知识。