Healthc Inform Res.  2011 Mar;17(1):24-28. 10.4258/hir.2011.17.1.24.

Evaluation of Co-occurring Terms in Clinical Documents Using Latent Semantic Indexing

• Han C
• Yoo S
• Choi J

• KMID: 2166592
• DOI: http://doi.org/10.4258/hir.2011.17.1.24

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.

Keyword

Information Storage and Retrieval; Cluster Analysis; Documentation

MeSH Terms

Abstracting and Indexing as Topic
Cluster Analysis
Information Storage and Retrieval
Periodicals
Semantics

Figure

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  Figure 1 Diagram showing a conceptual description of singular value decomposition.

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  Figure 2 Distributions of unique number of shared terms in editorials and clinical (Cli.) documents.

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