Determining prominent subdomains in medicine.
We discuss an automated method for identifying prominent subdomains in medicine. The motivation is to enhance the results of natural language processing by focusing on sublanguages associated with medical specialties concerned with prevalent disorders. At the core of our approach is a statistical system for topical categorization of medical text. A method based on epidemiological evidence is compared to another that considers frequency of occurrence of Medline citations. We suggest the isolation of UMLS terminology peculiar to individual medical specialties as a way of enhancing natural language processing systems in the biomedical domain.
Bernhardt
Powell J
PJ
Temple University, Philadelphia, Pennsylvania, USA.
Humphrey
Susanne M
SM
Rindflesch
Thomas C
TC
D000043:Abstracting and Indexing / Q000379:methods*; D004194:Disease / Q000145:classification; D001330:Electronic Data Processing; D016247:Information Storage and Retrieval; D046650:Medical Subject Headings; D008511:Medicine / Q000145:classification*; D009323:Natural Language Processing; D013038:Specialization; D009626:Terminology as Topic; D017432:Unified Medical Language System