Natural language processing for clinical and translational research

描述(由申请人提供)：大型电子病历(EMR)临床应用的快速增长导致用于临床和翻译研究的密集纵向数据集的可获得性前所未有地扩大。这种增长是由 最近的联邦立法，为积极应用和“有意义地使用”综合EMR的机构提供慷慨的财政激励。已经在努力将这些急诊医生联系起来，并将疾病发病和治疗结果的大规模研究的表型定义标准化，特别是在常规临床护理的背景下。然而，将EMR数据二次用于临床和翻译研究的一个众所周知的挑战是，许多详细的患者信息嵌入到叙述性文本中。自然语言处理(NLP)技术能够将非结构化的临床文本转换为编码数据，已被引入生物医学领域，并显示出良好的结果。研究人员使用NLP系统从放射学报告、出院摘要、问题清单、护理文档和医学教育文档中识别临床症状和常见的生物医学概念。不同的机构已经开发了不同的NLP系统，并利用这些系统将临床叙述文本转换为结构化数据，这些数据可用于其他临床应用和研究。将NLP应用于临床和转译研究的成功案例已被广泛报道。然而，机构经常部署不同的NLP系统，这会产生各种类型的输出格式，并使站点之间的信息交换变得困难。因此，不同临床NLP系统之间缺乏互操作性成为高效多点研究的瓶颈。此外，许多成功的研究通常需要一个强大的跨学科团队，其中信息学家和临床医生必须非常密切地合作，反复定义临床表型的最佳算法。由于密集的信息学支持可能不是每个临床研究人员都能获得的，NLP系统对最终用户的可用性是另一个重要的问题。拟议的项目建立在整个研究团队在临床和转化性研究项目中使用NLP的第一手知识和经验的基础上。临床和转化性研究有几个大的信息学倡议，但这些倡议通常假设一刀切，并遵循自上而下的方法来开发NLP解决方案。作为对这些举措的补充，我们将使用自下而上的方法来处理互操作性和可用性：i)我们将通过对现有NLP系统和NLP结果的经验分析，获得一个通用的NLP数据模型和交换格式；ii)我们将为NLP系统开发以用户为中心的前端界面，以与建议的NLP数据模型和交换格式保持一致，并将可用性分析纳入敏捷开发过程。所有交付成果将通过开放卫生NLP(OHNLP)联盟分发，我们打算使其更加开放和包容。

DESCRIPTION (provided by applicant): Rapid growth in the clinical implementation of large electronic medical records (EMRs) has led to an unprecedented expansion in the availability of dense longitudinal datasets for clinical and translational research. This growth is being fueled by recent federal legislation that provides generous financial incentives to institutions demonstrating aggressive application and "meaningful use" of comprehensive EMRs. Efforts are already underway to link these EMRs across institutions, and standardize the definition of phenotypes for large scale studies of disease onset and treatment outcome, specifically within the context of routine clinical care. However, a well-known challenge for secondary use of EMR data for clinical and translational research is that much of detailed patient information is embedded in narrative text. Natural Language Processing (NLP) technologies, which are able to convert unstructured clinical text into coded data, have been introduced into the biomedical domain and have demonstrated promising results. Researchers have used NLP systems to identify clinical syndromes and common biomedical concepts from radiology reports, discharge summaries, problem lists, nursing documentation, and medical education documents. Different NLP systems have been developed at different institutions and utilized to convert clinical narrative text into structured data that may be used for other clinical applications and studies. Successful stories in applying NLP to clinical and translational research have been reported widely. However, institutions often deploy different NLP systems, which produce various types of output formats and make it difficult to exchange information between sites. Therefore, the lack of interoperability among different clinical NLP systems becomes a bottleneck for efficient multi-site studies. In addition, many successful studies often require a strong interdisciplinary team where informaticians and clinicians have to work very closely to iteratively define optimal algorithms for clinical phenotypes. As intensive informatics support may not be available to every clinical researcher, the usability of NLP systems for end users is another important issue. The proposed project builds upon first-hand knowledge and experience across the research team in the use of NLP for clinical and translational research projects. There are several big informatics initiatives for clinical and translational research but those initiatives generally assume one shoe fits all and follow top-down approaches to develop NLP solutions. Complementary to those initiatives, we will use a bottom-up approach to handle interoperability and usability: i) we will obtain a common NLP data model and exchange format through empirical analysis of existing NLP systems and NLP results; ii) we will develop a user-centric NLP front end interface for NLP systems wrapped to be consistent with the proposed NLP data model and exchange format incorporating usability analysis into the agile development process. All deliverables will be distributed through the open health NLP (OHNLP) consortium which we intend to make it more open and inclusive.