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: 8133360
• 负责人: WENDY W. CHAPMAN
• 金额: $ 66.46万
• 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 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8133360
• 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. PUBLIC HEALTH RELEVANCE: In this project, we will develop a publicly available corpus of annotated clinical texts for NLP research. We will experiment with methods for increasing the efficiency of annotation and will annotate de-identified reports of nine types for linguistic and clinical information. In addition, we will create an NLP toolkit that can be shared and will evaluate it against other NLP systems in a shared task evaluation with the community.

描述（由申请人提供）：准确的临床研究、积极的决策支持和广泛覆盖的监测所需的大部分临床信息都被锁定在电子病历 (EMR) 的文本文件中。利用这些信息进行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。在过去的二十年里，多个研究小组开发了用于临床笔记的 NLP 工具，但阻碍临床 NLP 进展的一个主要瓶颈是缺乏用于训练和评估 NLP 应用的标准注释数据集。如果没有这些标准，单个 NLP 应用程序就会大量存在，无法根据标准注释训练不同的算法、共享和集成 NLP 模块或比较性能。我们建议开发标准和基础设施，使技术能够从文本医疗记录中提取科学信息，并且我们建议将这项研究作为全美 NLP 专家参与的合作项目。为了实现这一目标，我们将实现三个具体目标：目标 1：扩展现有标准并制定新的共识标准，以可互操作、可扩展和可用的方式注释临床文本。目标 2：应用现有方法和工具，并在必要时开发新方法和工具，以高效、准确的方式手动注释一组公开可用的临床文本。目标 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执行共享评估来评估该工具包。 公共健康相关性：在这个项目中，我们将为 NLP 研究开发一个公开的带注释的临床文本语料库。我们将尝试提高注释效率的方法，并对九种类型的去识别化报告进行语言和临床信息注释。此外，我们将创建一个可以共享的 NLP 工具包，并在与社区的共享任务评估中针对其他 NLP 系统进行评估。