title: Knowledge requirements for automated inference of medical textbook markup.
abstract: Indexing medical text in journals or textbooks requires a tremendous amount of resources. We tested two algorithms for automatically indexing nouns, noun-modifiers, and noun phrases, and inferring selected binary relations between UMLS concepts in a textbook of infectious disease. Sixty-six percent of nouns and noun-modifiers and 81% of noun phrases were correctly matched to UMLS concepts. Semantic relations were identified with 100% specificity and 94% sensitivity. For some medical sub-domains, these algorithms could permit expeditious generation of more complex indexing.
authors.affiliation: Veterans Affairs Palo Alto Health Care System, CA, USA.
authors.forename: D C
authors.initials: DC
authors.lastname: Berrios
authors.forename: A
authors.initials: A
authors.lastname: Kehler
authors.forename: L M
authors.initials: LM
authors.lastname: Fagan
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001330:Electronic Data Processing; D013782:Textbooks as Topic; D017432:Unified Medical Language System