Real-time Disambiguation of Abbreviations in Clinical Notes

临床记录中缩写词的实时消歧

• 批准号: 7866149
• 负责人: HUA XU
• 金额: $ 38.75万
• 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 2013-05-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7866149
• 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）：高质量医疗服务的一个关键先决条件是医疗机构内部和之间的有效沟通。然而，沟通可能会受到临床笔记中普遍使用的缩写的阻碍。临床医生在记录过程中使用缩写以节省时间。虽然缩写对作者来说似乎是明确的，但它们经常会对其他读者造成混淆，包括医疗保健提供者，患者和试图从文本中提取临床术语的自然语言处理（NLP）系统。虽然人们普遍认为缩写会导致错误，但很少有人为这个重要问题部署实用的解决方案。拟议的项目将开发，评估和共享临床缩略语识别和消歧（CARD）的系统方法，并在这样做的过程中，主要目的是使现有的NLP系统受益，并通过实时减少电子记录中的歧义来改善基于计算机的文档系统。本研究的具体目标包括以下五个方面：1）开发从临床文本语料库中自动检测缩略语及其词义的方法，建立一个全面的临床缩略语知识库; 2）开发和评估三个自动词义消歧（WSD）分类器，并建立联合收割机组合这些分类器的方法，以最大限度地提高它们的性能和覆盖率; 3）开发CARD系统，并通过将其与两个已建立的NLP系统集成来证明其有效性（MedLEE和KnowledgeMap）; 4）将CARD与机构临床文件系统集成（范德比尔特的StarNotes），并评估其在临床医生生成记录时实时扩展缩写的能力; 5）分发CARD知识库和软件用于非商业用途。