Annotation, development and evaluation for clinical information extraction

临床信息提取的注释、开发和评估

• 批准号: 8501543
• 负责人: WENDY W. CHAPMAN
• 金额: $ 37.02万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8501543
• 关键词: Address; Algorithms; Automated Annotation; Clinical; Clinical Research; Code; Communities; Computerized Medical Record; Consensus; Country; Data Set; Development; Disease; Evaluation; Goals; Gold; Guidelines; Individual; Judgment; Knowledge; Linguistics; Manuals; Medical Records; Methodology; Methods; Metric; Natural Language Processing; Performance; Reliance; Reporting; Research; Research Infrastructure; Research Personnel; Signs and Symptoms; System; Technology; Terminology; Text; Training; Translational Research; Translations; base; clinical care; cost; design; flexibility; innovation; knowledge translation; phrases; prevent; public health relevance; research clinical testing; research study; tool

DESCRIPTION (provided by applicant): Much of the clinical information required for accurate clinical research, active decision support, and broad-coverage surveillance is locked in text files in an electronic medical record (EMR). The only feasible way to leverage this information for translational science is to extract and encode the information using natural language processing (NLP). Over the last two decades, several research groups have developed NLP tools for clinical notes, but a major bottleneck preventing progress in clinical NLP is the lack of standard, annotated data sets for training and evaluating NLP applications. Without these standards, individual NLP applications abound without the ability to train different algorithms on standard annotations, share and integrate NLP modules, or compare performance. We propose to develop standards and infrastructure that can enable technology to extract scientific information from textual medical records, and we propose the research as a collaborative effort involving NLP experts across the U.S. To accomplish this goal, we will address three specific aims: Aim 1: Extend existing standards and develop new consensus standards for annotating clinical text in a way that is interoperable, extensible, and usable. Aim 2: Apply existing methods and tools, and develop new methods and tools where necessary for manually annotating a set of publicly available clinical texts in a way that is efficient and accurate. Aim 3: Develop a publicly available toolkit for automatically annotating clinical text and perform a shared evaluation to evaluate the toolkit, using evaluation metrics that are multidimensional and flexible.

描述（由申请人提供）：准确的临床研究，主动决策支持和宽覆盖监视所需的许多临床信息都锁定在电子病历（EMR）中的文本文件中。利用这些信息用于转化科学的唯一可行方法是使用自然语言处理（NLP）提取和编码信息。在过去的二十年中，几个研究小组为临床注释开发了NLP工具，但是防止临床NLP进展的主要瓶颈是缺乏用于培训和评估NLP应用程序的标准，注释的数据集。没有这些标准，各个NLP应用程序就有很多，而没有能够在标准注释，共享和集成NLP模块或比较性能上训练不同算法的能力。我们建议开发标准和基础设施，以使技术能够从文本医疗记录中提取科学信息，我们将这项研究作为一项协作努力，涉及美国各地的NLP专家以实现这一目标，我们将解决三个具体的目标：AIM 1：扩展现有标准，扩展现有标准，并在某种程度上开发新的共识标准，以互动，可互动，并且可以互动，并且可以互动，并且可以使用。目标2：应用现有的方法和工具，并在必要时开发新的方法和工具，以便以高效和准确的方式手动注释一组公开可用的临床文本。 AIM 3：开发一个公开可用的工具包，用于自动注释临床文本，并使用多维且灵活的评估指标进行共享评估以评估工具包。