title: LexValueSets: an approach for context-driven value sets extraction.
abstract: The ability to model, share and re-use value sets across medical information systems is an important requirement. However, generating value sets semi-automatically from a terminology service is an unresolved issue, in part due to the lack of linkage to clinical context patterns that provide the constraints in defining a concept domain and invocation of value sets extraction. Towards this goal, we develop and evaluate an approach for context-driven automatic value sets extraction based on a formal terminology model. The crux of the technique is to identify and define the context patterns from various domains of discourse and leverage them for value set extraction using two complementary ideas based on (i) local terms provided by the Subject Matter Experts (extensional) and (ii) Semantic definition of the concepts in coding schemes (intensional). A prototype was implemented based on SNOMED CT rendered in the LexGrid terminology model and a preliminary evaluation is presented.
authors.affiliation: Division of Biomedical Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA.
authors.forename: Jyotishman
authors.initials: J
authors.lastname: Pathak
authors.forename: Guoqian
authors.initials: G
authors.lastname: Jiang
authors.forename: Sridhar O
authors.initials: SO
authors.lastname: Dwarkanath
authors.forename: James D
authors.initials: JD
authors.lastname: Buntrock
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
authors.forename: Chris
authors.initials: C
authors.lastname: Chute
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D013358:Subject Headings; D014481:United States