NARURAL LANGUAGE PROCESSING OF MEDICAL REPORTS

医疗报告的自然语言处理

• 批准号: 6430473
• 负责人: RICKY K. TAIRA
• 金额: $ 23.16万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-04-01 至 2002-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6430473
• 关键词: automated medical record system; computer system design /evaluation; health care facility information system; human data; image processing; information retrieval; language; radiology; telemedicine; vocabulary development for information system

Medical reports contain a great deal of information that characterizes a patient's medical condition. A large percent of this information, however, is unstructured in the form of free text. In this form, the information is difficult to search, sort, analyze, summarize and present. We present a new method of natural language processing which attempts to automatically extract the important concepts from medical free text reports. The sentence parsing algorithm is based on a "field theory" that is motivated by how a multi-particle system forms attachments as explained in physics. This view of natural language processing attempts to view words as active entities rather than passive data elements. A model for how and why word attachments occur is presented. The model presents explanations in terms of both energy fields that characterize the stability of a word at a given state of the sentence parse and in terms of a signal processing model involving a resonance phenomenon. Our hypotheses are that: 1) the system can extract information from medical reports; 2) the results can be represented in a canonical form with respect to meaning; 3) the system is extendable to many domains of medicine. The testing and evaluation of our system will be performed on reports from the specific domains of radiology. We will explore the adaptability of the NLP system to the domains of pediatric pathology and urology. In addition to technical measures, NLP will be evaluated from the end-user perspective including the selection of appropriate imaging protocols, SNOMED coding, and information retrieval from radiology teaching cases.

医疗报告包含大量表征患者健康状况的信息。然而，这些信息的很大一部分都是非结构化的自由文本形式。在这种形式下，信息难以搜索、整理、分析、总结和呈现。我们提出了一种新的自然语言处理方法，尝试从医学自由文本报告中自动提取重要概念。句子解析算法基于“场论”，其动机是多粒子系统如何形成物理学中解释的附件。这种自然语言处理的观点试图将单词视为主动实体而不是被动数据元素。提出了单词附件如何以及为何发生的模型。该模型根据表征句子解析给定状态下单词稳定性的能量场和涉及共振现象的信号处理模型来提供解释。我们的假设是：1）系统可以从医疗报告中提取信息； 2）结果可以用关于含义的规范形式表示； 3）该系统可扩展到医学的许多领域。我们的系统的测试和评估将根据放射学特定领域的报告进行。我们将探索 NLP 系统在儿科病理学和泌尿学领域的适应性。除了技术措施外，NLP 还将从最终用户的角度进行评估，包括选择合适的成像协议、SNOMED 编码以及从放射学教学案例中检索信息。