title: Locating relevant patient information in electronic health record data using representations of clinical concepts and database structures.
abstract: Clinicians and clinical researchers often seek information in electronic health records (EHRs) that are relevant to some concept of interest, such as a disease or finding. The heterogeneous nature of EHRs can complicate retrieval, risking incomplete results. We frame this problem as the presence of two gaps: 1) a gap between clinical concepts and their representations in EHR data and 2) a gap between data representations and their locations within EHR data structures. We bridge these gaps with a knowledge structure that comprises relationships among clinical concepts (including concepts of interest and concepts that may be instantiated in EHR data) and relationships between clinical concepts and the database structures. We make use of available knowledge resources to develop a reproducible, scalable process for creating a knowledge base that can support automated query expansion from a clinical concept to all relevant EHR data.
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD ; Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD.
authors.forename: Xuequn
authors.initials: X
authors.lastname: Pan
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD ; Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD.
authors.forename: James J
authors.initials: JJ
authors.lastname: Cimino
mesh_terms: D064229:Biological Ontologies; D019992:Databases as Topic; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D051188:Knowledge Bases; D008193:Lyme Disease; D039061:Systematized Nomenclature of Medicine