Terminology model discovery using natural language processing and visualization techniques

Authors:
Li Zhou;Ying Tao;James J. Cimino;Elizabeth S. Chen;Hongfang Liu;Yves A. Lussier;George Hripcsak;Carol Friedman
Affiliations:
Department of Biomedical Informatics, Columbia University, New York, NY, USA;Department of Biomedical Informatics, Columbia University, New York, NY, USA;Department of Biomedical Informatics, Columbia University, New York, NY, USA;Department of Biomedical Informatics, Columbia University, New York, NY, USA;Department of Information Systems, University of Maryland, Baltimore, MD, USA;Department of Biomedical Informatics, Columbia University, New York, NY, USA;Department of Biomedical Informatics, Columbia University, New York, NY, USA;Department of Biomedical Informatics, Columbia University, New York, NY, USA
Venue:
Journal of Biomedical Informatics
Year:
2006

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Abstract

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.