Natural language processing for clinical and translational research

用于临床和转化研究的自然语言处理

基本信息

批准号：
8640959
负责人：
HONGFANG LIU
金额：
$ 58.01万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-04-01 至 2017-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8640959
关键词：
Acceleration Adopted Adoption Adverse drug effect Algorithms Architecture Attention Clinical Clinical Investigator Clinical Research Code Communities Computerized Medical Record DNA Databases Data Data Set Development Dictionary Discipline of Nursing Disease Association Documentation Elements Exclusion Criteria Genes Genomics Goals Growth Health Informatics Institution Knowledge Link Logical Observation Identifiers Names and Codes Manuals Medical Education Modeling Natural Language Processing Onset of illness Output Patients Pharmacogenomics Phenotype Play Process Radiology Specialty Reporting Research Research Personnel Research Project Grants Resources Role SNOMED Clinical Terms Semantics Shoes Site Solutions Statutes and Laws Structure Syndrome System Technology Text Translational Research Treatment outcome Work base clinical application clinical care clinical phenotype computer human interaction data exchange data modeling experience financial incentive flexibility human centered computing interoperability novel open source patient safety public health relevance rapid growth success tool usability user-friendly

项目摘要

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.

描述（由申请人提供）：大型电子病历（EMRS）的临床实施快速增长，导致了用于临床和转化研究的密集纵向数据集的空前扩展。这种增长正在推动最近的联邦立法为机构提供了慷慨的财务激励措施，这些机构证明了积极的应用和“有意义地使用”全面的EMR。已经努力将这些EMR跨机构联系起来，并标准化表型的定义，以进行大规模的疾病发作和治疗结果研究，特别是在常规临床护理的背景下。但是，对于将EMR数据用于临床和转化研究的二次使用的众所周知的挑战是，叙事文本中嵌入了许多详细的患者信息。能够将非结构化临床文本转换为编码数据的自然语言处理（NLP）技术已被引入生物医学领域，并显示出令人鼓舞的结果。研究人员使用NLP系统来鉴定放射学报告，出院摘要，问题清单，护理文档和医学教育文件中的临床综合症和常见的生物医学概念。已经在不同机构开发了不同的NLP系统，并用于将临床叙事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。已广泛报道了将NLP应用于临床和转化研究的成功故事。但是，机构经常部署不同的NLP系统，这些系统会产生各种类型的输出格式，并使网站之间的信息很难交换。因此，不同临床NLP系统之间缺乏互操作性成为有效多站点研究的瓶颈。此外，许多成功的研究通常需要一个强大的跨学科团队，在该团队中，信息医生和临床医生必须非常紧密地与迭代界定义临床表型的最佳算法。由于每个临床研究人员都可能无法获得密集的信息学支持，因此NLP系统对最终用户的可用性是另一个重要问题。拟议的项目基于整个研究团队的第一手知识和经验，用于将NLP用于临床和转化研究项目。有几项针对临床和转化研究的大型信息学计划，但是这些举措通常假设一只鞋适合所有人，并遵循自上而下的方法来开发NLP解决方案。与这些举措相辅相成，我们将使用自下而上的方法来处理互操作性和可用性：i）通过对现有NLP系统和NLP结果的经验分析，我们将获得常见的NLP数据模型和交换格式； ii）我们将开发一个以用户为中心的NLP前端界面，用于包含的NLP系统，该系统与建议的NLP数据模型和交换格式一致，该模型和交换格式将可用性分析纳入敏捷开发过程。所有可交付成果将通过开放健康的NLP（OHNLP）财团分发，我们打算使其更开放和包容。