Terminology model discovery using natural language processing and visualization techniques.
Terminology model discovery using natural language processing and visualization techniques.
复制标题
使用自然语言处理和可视化技术发现术语模型。
DOI：
10.1016/j.jbi.2005.10.006
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发表时间：
2006
影响因子：
4.5
通讯作者：
Friedman,Carol
中科院分区：
文献类型：
--
作者：
Zhou,Li;Tao,Ying;Cimino,JamesJ;Chen,ElizabethS;Liu,Hongfang;Lussier,YvesA;Hripcsak,George;Friedman,Carol
医学术语对于临床信息的明确编码和交换非常重要。开发术语模型的传统手动方法非常耗时，并且开发人员可以检查的短语数量有限。在本文中，我们提出了一种自动化的方法来开发基于自然语言处理（NLP）和信息可视化技术的医学术语模型。选择手术病理报告作为开发病理程序术语模型的测试语料库。使用一般的NLP处理器的医疗领域，MedLEE，提供了一种自动化的方法，从自由文本语料库获取语义结构，并揭示了一个新的高通量的方法，医学术语模型开发。信息可视化技术的使用支持从医学文档生成的大量语义结构的概括和可视化。我们相信，基于自然语言处理和信息可视化的通用方法将有助于医学术语建模。
Medical terminologies are important for unambiguous encoding and exchange of clinical information. The traditional manual method of developing terminology models is time-consuming and limited in the number of phrases that a human developer can examine. In this paper, we present an automated method for developing medical terminology models based on natural language processing (NLP) and information visualization techniques. Surgical pathology reports were selected as the testing corpus for developing a pathology procedure terminology model. The use of a general NLP processor for the medical domain, MedLEE, provides an automated method for acquiring semantic structures from a free text corpus and sheds light on a new high-throughput method of medical terminology model development. The use of an information visualization technique supports the summarization and visualization of the large quantity of semantic structures generated from medical documents. We believe that a general method based on NLP and information visualization will facilitate the modeling of medical terminologies.