Zhou Li, Tao Ying, Cimino James J, Chen Elizabeth S, Liu Hongfang, Lussier Yves A, Hripcsak George, Friedman Carol
Department of Biomedical Informatics, Columbia University, New York, NY, USA.
J Biomed Inform. 2006 Dec;39(6):626-36. doi: 10.1016/j.jbi.2005.10.006.
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.
医学术语对于临床信息的明确编码和交换至关重要。传统的手动开发术语模型的方法既耗时，而且人类开发者能够检查的短语数量也有限。在本文中，我们提出了一种基于自然语言处理（NLP）和信息可视化技术来开发医学术语模型的自动化方法。手术病理报告被选作开发病理程序术语模型的测试语料库。使用用于医学领域的通用NLP处理器MedLEE，提供了一种从自由文本语料库中获取语义结构的自动化方法，并为医学术语模型开发的新高通量方法提供了思路。信息可视化技术的使用支持对从医学文档中生成的大量语义结构进行汇总和可视化。我们相信基于NLP和信息可视化的通用方法将有助于医学术语的建模。
