Real-time Disambiguation of Abbreviations in Clinical Notes

临床记录中缩写词的实时消歧

• 批准号: 8305149
• 负责人: HUA XU
• 金额: $ 12.9万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-31 至 2012-10-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8305149
• 关键词: Abbreviations; Algorithms; Architecture; Caring; Cessation of life; Clinical; Communication; Computer Systems; Computer software; Computers; Confusion; Coronary Arteriosclerosis; Databases; Detection; Disease; Documentation; Effectiveness; Electronics; Equipment and supply inventories; Frequencies; Health Personnel; Healthcare; Individual; Informatics; Joints; Machine Learning; Manuals; Medical Records; Methods; Names; Natural Language Processing; Nitroglycerin; Patients; Performance; Pharmaceutical Preparations; Physicians; Process; Provider; Reader; Records; Serious Adverse Event; Solutions; System; Technology; Text; Time; Work; Writing; acronyms; base; health care delivery; health record; improved; innovation; insight; knowledge base; novel; phrases; satisfaction

DESCRIPTION (provided by applicant): A key prerequisite for high-quality healthcare delivery is effective communication within and across healthcare settings. However, communication can be hampered by the pervasive use of abbreviations in clinical notes. Clinicians use abbreviations to save time during documentation. While abbreviations may seem unambiguous to their authors, they often cause confusion to other readers, including healthcare providers, patients, and natural language processing (NLP) systems attempting to extract clinical terms from text. While the understanding that abbreviations can cause errors is widespread, few have deployed pragmatic solutions for this important problem. The proposed project will develop, evaluate, and share a systematic approach to Clinical Abbreviation Recognition and Disambiguation (CARD), and in doing so substantially aims to benefit existing NLP systems and to improve computer-based documentation systems by reducing ambiguities in electronic records in real-time. The study includes the following five Specific Aims: 1) Develop automated methods to detect abbreviations and their senses from clinical text corpora and build a comprehensive knowledge base of clinical abbreviations; 2) Develop and evaluate three automated word sense disambiguation (WSD) classifiers, and establish methods to combine those classifiers to maximize both their performance and coverage; 3) Develop the CARD system, and demonstrate its effectiveness by integrating it with two established NLP systems (MedLEE and KnowledgeMap); 4) Integrate CARD with an institutional clinical documentation system (Vanderbilt's StarNotes) and evaluate its ability to expand abbreviations in real-time as clinicians generate records; 5) Distribute the CARD knowledge base and software for non-commercial uses.

描述（由申请人提供）：高质量医疗保健提供的关键先决条件是在医疗机构内部和跨医疗机构内部的有效沟通。但是，通过在临床注释中普遍使用缩写，可以阻碍沟通。临床医生使用缩写来节省文档期间的时间。尽管对作者的缩写似乎显得明确，但它们通常会使其他读者混淆，包括医疗保健提供者，患者和自然语言处理（NLP）系统，试图从文本中提取临床术语。尽管对缩写会导致错误的理解是普遍存在的，但很少有人为这个重要问题部署了务实的解决方案。拟议的项目将开发，评估和共享一种系统的临床缩写识别和歧义（CARD）的系统方法，并以实质性的旨在使现有的NLP系统受益并改善基于计算机的文档系统，从而通过实时降低电子记录中的歧义。该研究包括以下五个具体目的：1）开发自动化方法来检测缩写及其从临床文本语料库中的感觉，并建立临床缩写的全面知识基础； 2）开发和评估三个自动化的单词识别歧义（WSD）分类器，并建立结合这些分类器以最大化其性能和覆盖范围的方法； 3）开发卡系统，并通过将其与两个已建立的NLP系统（Medlee和Knowledgemap）集成来证明其有效性； 4）将卡与机构临床文档系统（Vanderbilt的Starnotes）集成在一起，并评估其在临床医生产生记录时实时扩展缩写的能力； 5）为非商业用途分发卡知识库和软件。