Challenges in Natural Language Processing for Clinical Narratives

描述(由申请人提供)：电子健康记录(EHR)的描述包含难以自动提取、索引、搜索或解释的有用信息。 用于自动提取、索引、搜索和解释电子病历的临床自然语言处理(NLP)技术正在开发中；然而，由于与电子病历相关的隐私问题，这些技术通常是由在特定机构中有特权访问电子病历的团队开发的。为特定机构的一组特定数据量身定做的技术会对该数据产生鼓舞人心的结果；然而，它们可能无法推广到来自其他机构的类似数据，甚至同一机构的其他部门。因此，从这些技术中学习并在其基础上进行建设变得困难。 为了改进EHR中的NLP，需要对能够处理相同数据集上的给定任务的方法进行逐个比较。共享任务提供了一种进行系统的面对面比较的方法。该提案描述了一系列共同的任务挑战和会议，这些挑战和会议在五年内展开，通过根据数据使用协议向广泛的研究界分发不确定的EHR来促进尖端临床NLP系统的开发和评估，从而： *可以识别和推进临床NLP技术的最先进技术， *提供了一套能够使用电子病历叙述中所含信息的技术，以及 *可以使来自EHR叙述的信息更容易获取，例如，用于临床和医学研究。 支持组织共享任务挑战的科学活动部分由Informatics for集成生物学和床边(I2b2)赞助，资助号为U54-LM008748，PI：Kohane。 这项提议旨在组织一系列研讨会、会议记录和期刊特刊，这些专题将伴随着共同任务挑战，以传播挑战产生的知识。

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.