Zou Qinghua, Chu Wesley W, Morioka Craig, Leazer Gregory H, Kangarloo Hooshang
Dept of Computer Science, University of California, Los Angeles, USA.
AMIA Annu Symp Proc. 2003;2003:763-7.
Extracting key concepts from clinical texts for indexing is an important task in implementing a medical digital library. Several methods are proposed for mapping free text into standard terms defined by the Unified Medical Language System (UMLS). For example, natural language processing techniques are used to map identified noun phrases into concepts. They are, however, not appropriate for real time applications. Therefore, in this paper, we present a new algorithm for generating all valid UMLS concepts by permuting the set of words in the input text and then filtering out the irrelevant concepts via syntactic and semantic filtering. We have implemented the algorithm as a web-based service that provides a search interface for researchers and computer programs. Our preliminary experiment shows that the algorithm is effective at discovering relevant UMLS concepts while achieving a throughput of 43K bytes of text per second. The tool can extract key concepts from clinical texts for indexing.
从临床文本中提取关键概念用于索引是实现医学数字图书馆的一项重要任务。人们提出了几种方法将自由文本映射到由统一医学语言系统（UMLS）定义的标准术语中。例如，自然语言处理技术被用于将识别出的名词短语映射为概念。然而，它们并不适用于实时应用。因此，在本文中，我们提出了一种新算法，通过对输入文本中的单词集进行排列来生成所有有效的UMLS概念，然后通过句法和语义过滤去除不相关的概念。我们已将该算法实现为一个基于网络的服务，为研究人员和计算机程序提供搜索界面。我们的初步实验表明，该算法在发现相关UMLS概念方面是有效的，同时实现了每秒43KB文本的吞吐量。该工具可以从临床文本中提取关键概念用于索引。
