NDLI: Converting Semi-structured Clinical Medical Records into Information and Knowledge

Content Provider	IEEE Xplore Digital Library
Author	Xiaohua Zhou Hyoil Han Chankai, I. Prestrud, A.A. Brooks, A.D.
Copyright Year	2005
Description	Author affiliation: College of Information Science and Technology, Drexel University (Xiaohua Zhou)
Abstract	Clinical medical records contain a wealth of information, largely in free-textual form. Thus, means to extract structured information from free-text records becomes an important research endeavor. In this paper, we propose and implement an information extraction system that extracts three types of information - numeric values, medical terms and categorical value - from semi-structured patient records. Three approaches are proposed to solve the problems posed by each of the three types of values, respectively, and very good performance (precision and recall) is achieved. A novel link-grammar based approach was invented to associate feature and number in a sentence, and extremely high accuracy was achieved. A simple but efficient approach, using POS-based pattern and domain ontology, was adopted to extract medical terms of interest. Finally, an NLPbased feature extraction method coupled with an ID3 based decision tree is used to classify and extract categorical cases. This preliminary approach to categorical fields has, so far, proven to be quite effective.
Starting Page	1162
Ending Page	1162
File Size	213386
Page Count	1
File Format	PDF
ISBN	0769526578
DOI	10.1109/ICDE.2005.207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-04-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information science Databases Surgery Ontologies Educational institutions Feature extraction Data mining History Decision trees Classification tree analysis
Content Type	Text
Resource Type	Article

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