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Exploring Text Mining from MEDLINE
We present a text mining application that exploits the MeSH heading subheading combinations present in MEDLINE records. The process begins with a user specified pair of subheadings. Co-occurring concepts qualified by these subheadings are regarded as being conceptually related and thus extracted. A parallel process using SemRep, a linguistic tool, also extracts conceptually related concept pairs from the titles of MEDLINE records. The pairs extracted via MeSH and the pairs extracted via SemRep are compared to yield a high confidence subset. These pairs are then combined to project a summary view associated with the selected subheading pair. For each concept the "diversity" in the set of related concepts is assessed. We suggest that this summary and the diversity indicators will be useful a health care practitioner or researcher. We illustrate this application with the subheading pair "drug therapy" and "therapeutic use" which approximates the treatment relationship between Drugs and Diseases.