Extracting semantic predications from Medline citations for pharmacogenomics.
We describe a natural language processing system (Enhanced SemRep) to identify core assertions on pharmacogenomics in Medline citations. Extracted information is represented as semantic predications covering a range of relations relevant to this domain. The specific relations addressed by the system provide greater precision than that achievable with methods that rely on entity co-occurrence. The development of Enhanced SemRep is based on the adaptation of an existing system and crucially depends on domain knowledge in the Unified Medical Language System. We provide a preliminary evaluation (55% recall and 73% precision) and discuss the potential of this system in assisting both clinical practice and scientific investigation.
Ahlers
Caroline B
CB
Lister Hill National Center for Biomedical Communications, National Library of Medicine Bethesda, Maryland 20894, USA.
Fiszman
Marcelo
M
Demner-Fushman
Dina
D
Lang
François-Michel
FM
Rindflesch
Thomas C
TC
D019295:Computational Biology; D019389:Cytochrome P-450 CYP2D6 / Q000235:genetics / Q000378:metabolism; D006801:Humans; D016239:MEDLINE; D009323:Natural Language Processing; D010597:Pharmacogenetics / Q000706:statistics & numerical data*; D012660:Semantics; D017432:Unified Medical Language System
D019389:Cytochrome P-450 CYP2D6