Natural language processing for clinical and translational research

用于临床和转化研究的自然语言处理

• 批准号: 8920720
• 负责人: HONGFANG LIU
• 金额: $ 16万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 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/8920720
• 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; rapid growth; success; tool; usability; user-friendly

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（EMR）临床实施的快速增长导致临床和转化研究的密集纵向数据集的可用性空前扩大。这一增长的推动因素是 最近的联邦立法为积极应用和“有意义地使用”综合电子病历的机构提供了慷慨的财政激励。人们已经在努力将这些 EMR 跨机构联系起来，并对疾病发作和治疗结果的大规模研究（特别是在常规临床护理的背景下）的表型定义进行标准化。然而，将 EMR 数据二次用于临床和转化研究的一个众所周知的挑战是，许多详细的患者信息都嵌入在叙述文本中。自然语言处理（NLP）技术能够将非结构化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有希望的结果。研究人员使用 NLP 系统从放射学报告、出院总结、问题列表、护理文件和医学教育文件中识别临床综合征和常见生物医学概念。不同的机构开发了不同的 NLP 系统，并用于将临床叙述文本转换为可用于其他临床应用和研究的结构化数据。将 NLP 应用于临床和转化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，从而产生各种类型的输出格式，并且使得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中心研究的瓶颈。此外，许多成功的研究通常需要强大的跨学科团队，信息学家和临床医生必须密切合作，迭代地定义临床表型的最佳算法。由于并非每个临床研究人员都可以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。拟议的项目建立在整个研究团队将 NLP 用于临床和转化研究项目的第一手知识和经验的基础上。有几个针对临床和转化研究的大型信息学计划，但这些计划通常假设一种方法适合所有情况，并遵循自上而下的方法来开发 NLP 解决方案。作为这些举措的补充，我们将采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 结果的实证分析，获得通用的 NLP 数据模型和交换格式； ii) 我们将为 NLP 系统开发一个以用户为中心的 NLP 前端界面，使其与提议的 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健康 NLP (OHNLP) 联盟分发，我们打算使其更加开放和包容。