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（EHR）的叙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EHR的临床自然语言处理（NLP）技术正在开发中;然而，由于与EHR相关的隐私问题，此类技术通常由特定机构中有权访问EHR的团队开发。针对特定机构的特定数据集量身定制的技术可以在该数据上产生令人鼓舞的结果;然而，它们可能无法推广到其他机构甚至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其基础上发展变得困难。 为了改进EHR中的NLP，需要对可以在同一数据集上解决给定任务的方法进行头对头比较。共享任务提供了一种进行系统性头对头比较的方法。该提案描述了一系列共享任务的挑战和会议，分布在五年的时间内，通过根据数据使用协议向广泛的研究社区分发去识别的EHR来促进尖端临床NLP系统的开发和评估，以便： * 临床NLP技术的最新发展可以被识别和推进， * 一套能够使用EHR叙述中包含的信息的技术， * EHR叙述中的信息可以变得更容易获取，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整合生物学和床边信息学（i2 b2）赞助，资助号U 54-LM 008748，PI：Kohane。 该提案旨在组织一系列讲习班、会议记录和期刊特刊，伴随共同任务挑战，以传播挑战产生的知识。