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Published Articles >> Table of Contents >> Abstract
21st International Conference on Data Engineering Workshops (ICDEW'05)
p. 1162
Converting Semi-structured Clinical Medical Records into Information and Knowledge
Xiaohua Zhou, College of Information Science and Technology, Drexel University
Hyoil Han, College of Information Science and Technology, Drexel University
Isaac Chankai, College of Medicine, Drexel University
Ann A. Prestrud, College of Medicine, Drexel University
Ari D. Brooks, College of Medicine, Drexel University
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2005.207
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| Abstract |
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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.
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 Additional Information
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Citation:
Xiaohua Zhou, Hyoil Han, Isaac Chankai, Ann A. Prestrud, Ari D. Brooks,
"Converting Semi-structured Clinical Medical Records into Information and Knowledge,"
icdew,
p. 1162,
21st International Conference on Data Engineering Workshops (ICDEW'05),
2005
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