UNLOCKING DATA FROM MEDICAL RECORDS WITH TEXT PROCESSING

描述（改编自摘要）： 该项目的长期目标是使用自然语言处理（NLP） 帮助实现电子病历（EMR）的全部潜力。 我们的研究涉及先进的NLP技术：1）提取和编码 文本报告中的信息; 2）将术语映射到权威词汇表; 3)基于域的处理获得全面的域覆盖 语料库;以及4）通过提供可视化来促进词汇发展 使用可扩展标记语言（XML）的工具。 它已经被 证明了MedLEE，我们开发的NLP系统，准确地提取和 将信息编入电子病历。 目前的项目建立在我们的 使用MedLEE的经验，并使用它来实现后三个目标 关于词汇的发展和标准化。 更具体地说，MedLEE将用于将源术语映射到UMLS概念。 MedLEE将处理和构建源术语和候选UMLS 理念的 将根据结构相似性找到合适的匹配 在源术语的组成部分和候选概念之间。 这应该 增强当前的方法，因为了解匹配的修饰符类型 应该提高比赛的质量。 我们还将使用MedLEE来处理 一个大型语料库，并生成XML格式的结构化输出。 基于统计的 将计算结构化输出，然后与临床相关 将基于结构的频率来检测复合项 包含更多的基本术语。 我们的方法不同于其他发现 方法，因为我们使用NLP技术来识别语义修饰符， 复杂的关系，即使条款是远离对方，而其他 方法使用基于邻近性的统计同现数据。 个人 XML结构和统计信息将被组合并映射到单个XML中 树 可以使用XML可视化树和频率 树查看器，用于导航树、操作树和重新组织 根据不同的轴（即，手术过程尸体位置 发现）。 使用复杂的NLP系统，如MedLEE，是理想的 我们建议的词汇发展工作的基础， 医学术语是医学语言的重要组成部分 和最先进的自然语言处理系统的状态，特别是配备处理 语言的内在复杂性。

DESCRIPTION (adapted from the Abstract): The long-term aim of this project is to use natural language processing (NLP) to help realize the full potential of the Electronic Medical Record (EMR). Our research involves advanced NLP techniques to: 1) extract and encode information in textual reports; 2) map terms to an authoritative vocabulary; 3) obtain comprehensive domain coverage based on the processing of domain corpora; and 4) facilitate vocabulary development by providing visualization tools using the Extensible Markup Language (XML). It has already been demonstrated that MedLEE, the NLP system we developed, accurately extracts and codifies information in the EMR. This current project builds upon our experience with MedLEE and uses it to accomplish the latter three goals concerning vocabulary development and standardization. More specifically, MedLEE will be used to map source terms to UMLS concepts. MedLEE will process and structure the source terms and candidate UMLS concepts. Suitable matches will be found based on structural similarity between components of the source term and candidate concepts. This should enhance current methods because knowledge of the type of modifiers that match should improve the quality of the matches. We will also use MedLEE to process a large corpus and generate structured output in XML format. Statistics based on the structured output will be computed, and then clinically relevant composite terms will be detected based on frequencies of the structures containing the more elementary terms. Our method differs from other discovery methods because we use NLP techniques that identify semantic modifiers and complex relations even if the terms are distant from each other, whereas other methods use statistical co-occurrence data based on adjacency. The individual XML structures and statistics will be combined and mapped into a single XML tree. It will be possible to visualize the tree and frequencies using an XML tree viewer, to navigate the tree, to manipulate the tree, and to reorganize the tree according to different axes (i.e., procedure, body location, finding). The use of a sophisticated NLP system, such as MedLEE, is ideal as a foundation for our proposed work in vocabulary development and standardization; medical terminology is an integral part of medical language and a state of the art NLP system is especially equipped to handle the inherent complexities of language.