Challenges in Natural Language Processing in Clinical Text

临床叙述的自然语言处理挑战 翻译后摘要：电子健康记录（EHR）的叙述包含有用的信息，是很难 自动提取、索引、搜索或解释。临床自然语言处理（NLP）技术 电子健康档案的自动提取、索引、搜索和解释正在开发中;然而，由于 与EHR相关的隐私问题，此类技术通常由具有特权的团队开发， 获取特定机构的电子健康记录。根据给定的数据集定制的技术 机构根据这些数据得出了令人鼓舞结果;然而，它们可能无法从其他机构的类似数据中进行归纳 机构，甚至是同一机构的其他部门。因此，学习这些技术 在此基础上进行建设变得困难。 为了改善EHR中的NLP，需要对可以 在同一数据集上处理给定任务。共享任务提供了一种进行系统性头对- 头部比较此提案描述了一系列共享任务的挑战和会议，分布在 一个为期五年的时期，促进尖端临床NLP系统的开发和评估， 根据数据使用协议，向广泛的研究界分发去识别的EHR，以便： 确保临床NLP技术的最新技术可以被识别和推进， 一套能够使用EHR叙述中包含的信息的技术， 可用和 电子健康记录叙述中的信息可以更容易获得，例如，用于临床和 医学研究 支持组织共同任务挑战的科学活动部分由以下机构赞助： 整合生物学和床边的信息学（i2 b2），授权号U 54-LM 008748，PI：Kohane。CEGS NGRID，NIH P50 MH 106933，PI：Isaac Kohane也支持这些共享任务。 本提案旨在组织一系列研讨会、会议记录和期刊特刊 这将伴随着共同任务的挑战，以传播 挑战

Challenges in Natural Language Processing for Clinical Narratives Abstract: 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. CEGS NGRID, NIH P50 MH106933, PI: Isaac Kohane also supported these shared tasks. 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.