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Annotation, development and evaluation for clinical information extraction (transfer)

临床信息提取（传输）的注释、开发和评估

基本信息

批准号：
8868500
负责人：
WENDY W. CHAPMAN
金额：
$ 29.79万
依托单位：
BOSTON CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-01 至 2015-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8868500
关键词：
Annotation development evaluation clinical information

项目摘要

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.

准确的临床研究、主动决策所需的大量临床信息支持和广泛覆盖的监视被锁定在电子医疗的文本文件中记录（EMR）。利用这些信息进行翻译的唯一可行的方法科学是使用自然语言处理来提取和编码信息（自然语言处理）。在过去的二十年里，多个研究小组开发了 NLP 工具临床笔记，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 应用程序的标准带注释数据集。没有这些标准中，单独的 NLP 应用程序比比皆是，但无法训练不同的标准注释上的算法，共享和集成 NLP 模块，或进行比较表现。我们建议开发标准和基础设施，使从文本医疗记录中提取科学信息的技术，我们提议这项研究是全美 NLP 专家共同努力的结果为了实现这一目标，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临床注释共识标准以可互操作、可扩展和可用的方式编写文本。目标 2：应用现有的方法和工具，并开发新的方法和工具以手动方式注释一组公开可用的临床文本所必需的高效、准确。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执行使用评估指标来评估工具包的共享评估多维且灵活。