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

准确的临床研究所需的大量临床信息， 支持，和广泛覆盖的监视锁定在文本文件中的电子医疗 记录（EMR）。利用这些信息进行翻译的唯一可行的方法是 科学是使用自然语言处理来提取和编码信息 （NLP）。在过去的二十年里，一些研究小组开发了NLP工具 但临床NLP进展的主要瓶颈是缺乏 用于训练和评估NLP应用程序的标准注释数据集。没有 这些标准，个别NLP应用程序比比皆是，没有能力训练不同的 标准注释上的算法，共享和集成NLP模块，或比较 性能我们建议制定标准和基础设施， 从文本医疗记录中提取科学信息的技术，我们 建议将这项研究作为一项涉及美国各地NLP专家的合作努力。 为了实现这一目标，我们将致力于三个具体目标： 目标1： 扩展现有标准并制定新的共识标准，用于注释临床 以一种可互操作、可扩展和可用的方式提供文本。 目标二： 应用现有的方法和工具，并开发新的方法和工具， 这对于手动注释一组公开可用的临床文本是必要的， 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 一个共享的评估工具包，使用评估指标， 多维性和灵活性。

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.