Evaluation of Co-occurring Terms in Clinical Documents Using Latent Semantic Indexing / 대한의료정보학회지
Healthcare Informatics Research
;
: 24-28, 2011.
Article
in English
| WPRIM
| ID: wpr-106942
ABSTRACT
OBJECTIVES:
Measurement of similarities between documents is typically influenced by the sparseness of the term-document matrix employed. Latent semantic indexing (LSI) may improve the results of this type of analysis.METHODS:
In this study, LSI was utilized in an attempt to reduce the term vector space of clinical documents and newspaper editorials.RESULTS:
After applying LSI, document similarities were revealed more clearly in clinical documents than editorials. Clinical documents which can be characterized with co-occurring medical terms, various expressions for the same concepts, abbreviations, and typographical errors showed increased improvement with regards to a correlation between co-occurring terms and document similarities.CONCLUSIONS:
Our results showed that LSI can be used effectively to measure similarities in clinical documents. In addition, correlation between the co-occurrence of terms and similarities realized in this study is an important positive feature associated with LSI.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Semantics
/
Cluster Analysis
/
Information Storage and Retrieval
/
Periodical
/
Abstracting and Indexing
Language:
English
Journal:
Healthcare Informatics Research
Year:
2011
Type:
Article
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