Semantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context
AffiliationUniv Arizona, Dept Med
Univ Arizona, Inst BIO5
Univ Arizona, Ctr Biomed Informat & Biostat
Univ Arizona, Ctr Canc
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CitationSemantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context 2017, 2017:1 Journal of Healthcare Engineering
RightsCopyright © 2017 Jung-wei Fan et al. This is an open access article distributed under the Creative Commons Attribution License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractExposome is a critical dimension in the precision medicine paradigm. Effective representation of exposomics knowledge is instrumental to melding nongenetic factors into data analytics for clinical research. There is still limited work in (1) modeling exposome entities and relations with proper integration to mainstream ontologies and (2) systematically studying their presence in clinical context. Through selected ontological relations, we developed a template-driven approach to identifying exposome concepts from the Unified Medical Language System (UMLS). The derived concepts were evaluated in terms of literature coverage and the ability to assist in annotating clinical text. The generated semantic model represents rich domain knowledge about exposure events (454 pairs of relations between exposure and outcome). Additionally, a list of 5667 disorder concepts with microbial etiology was created for inferred pathogen exposures. The model consistently covered about 90% of PubMed literature on exposure-induced iatrogenic diseases over 10 years (2001–2010). The model contributed to the efficiency of exposome annotation in clinical text by filtering out 78% of irrelevant machine annotations. Analysis into 50 annotated discharge summaries helped advance our understanding of the exposome information in clinical text. This pilot study demonstrated feasibility of semiautomatically developing a useful semantic resource for exposomics.
NoteOpen access journal.
VersionFinal published version
SponsorsUniversity of Arizona Health Sciences CB2; BIO5 Institute; NIH [U01AI122275, HL132532, CA023074, 1UG3OD023171, 1R01AG053589-01A1, 1S10RR029030]; [U54LM008748]