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Challenges in Natural Language Processing in Clinical Text

临床文本自然语言处理的挑战

基本信息

批准号：
9597333
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：
2017
资助国家：
美国
起止时间：
2017-11-08 至 2018-11-07
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9597333
关键词：
Challenges Natural Language Processing Clinical

项目摘要

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.

临床叙事自然语言处理面临的挑战摘要：电子健康记录(EHR)的叙述包含了很难理解的有用信息自动提取、索引、搜索或解释。临床自然语言处理(NLP)技术 EHR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，此类技术通常是由拥有特权的团队开发的可以接触到特定机构的EHR。针对给定数据集中的特定数据量身定做的技术机构对数据产生鼓舞人心结果；然而，他们可能无法推广到来自其他机构的类似数据机构，甚至是来自同一机构的其他部门。因此，从这些技术中学习在它们的基础上建设变得困难起来。为了改善EHR中的NLP，需要对以下方法进行面对面的比较处理同一数据集上的给定任务。共享任务提供了一种进行系统面对面交流的方法人头比较。该提案描述了一系列共享任务挑战和会议，五年内，通过以下方式促进尖端临床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不确定的电子病历，以便： 临床NLP技术的最先进技术可以被识别和推进， 一组能够使用电子病历叙述中包含的信息的技术成为可用，并且 可以使来自电子病历叙述的信息更容易获取，例如，对于临床和医学研究。支持组织共同任务挑战的科学活动的部分赞助单位是生物学与床边整合信息学(I2b2)，授权号U54-LM008748，PI：Kohane。CEGS NGRID，NIH P50 MH106933，PI：Isaac Kohane也支持这些共享任务。该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挑战，以便传播由挑战。