NARURAL LANGUAGE PROCESSING OF MEDICAL REPORTS

医疗报告包含大量表征患者医疗状况的信息。然而，这些信息的很大一部分是以自由文本的形式非结构化的。在这种形式下，信息很难搜索，分类，分析，总结和呈现。本文提出了一种新的自然语言处理方法，试图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，而不是被动的数据元素。一个模型，如何和为什么字附件发生。该模型提出的解释，在两个能量场，表征一个词在一个给定的状态的句子解析的稳定性，并在涉及共振现象的信号处理模型。我们的假设是：1）该系统可以从医学报告中提取信息; 2）结果可以以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价将在来自放射学特定领域的报告上进行。我们将探讨自然语言处理系统在儿科病理学和泌尿学领域的适应性。除了技术措施，NLP将从最终用户的角度进行评估，包括选择适当的成像协议，SNOMED编码，以及从放射学教学案例中检索信息。

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.