Work

SMASH: A Data-driven Informatics Method to Assist Experts in Characterizing Semantic Heterogeneity among Data Elements.

Year: 2016

Type: article

Abstract: . An HIV expert assessed pairs for semantic equivalence. Heterogeneous DEs were either semantically-equivalent/syntactically-different (HIV-positive/HIV+/Seropositive), or syntactically-equivalent/semantically-different ("Partner" [sexual]/"Partner"[relationship]). Context of usage was considered. SMASH aided identification of SH. Of 1,175 DE from pairs, 1,048 (87%) were semantically heterogeneous and 127 (13%) were homogeneous. Most heterogeneous pairs (97%) were semantically-equivalent/syntactically-different. Expert-based and data-driven methods are complementary for assessing SH, especially among semantically-equivalent/syntactically-different DE. Similar expert-based/data-driven solutions are recommended for resolving SH.

Source: PubMed Central

Authors: William Brown, Chunhua Weng, David K Vawdrey, Alex Carballo-Diéguez, Suzanne Bakken

Institutions: New York State Psychiatric Institute, HIV Center for Clinical and Behavioral Studies, Columbia University

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Institute of Mental Health, U.S. National Library of Medicine

Awards: The Next Frontier in Diabetes Communication: Promoting Health Literacy in the Era of Secure Messaging, I3DEAL for HIV Research, Training in Biomedical Informatics at Columbia University, Center for AIDS Prevention Studies (CAPS), HIV Center for Clinical and Behavioral Studies

OpenAlex ID: W2620680248

FWCI: 0.173

Cites: 13

Cited by: 4

Related to: 10