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Journal of Healthcare Engineering
Volume 2017, Article ID 3818302, 10 pages
Research Article

Semantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context

Jung-wei Fan,1,2,3 Jianrong Li,1,2,3 and Yves A. Lussier1,2,3,4

1Department of Medicine, The University of Arizona, Tucson, AZ, USA
2BIO5 Institute, The University of Arizona, Tucson, AZ, USA
3Center for Biomedical Informatics & Biostatistics, The University of Arizona, Tucson, AZ, USA
4Cancer Center, The University of Arizona, Tucson, AZ, USA

Correspondence should be addressed to Jung-wei Fan; ude.anozira.liame@jnaf and Yves A. Lussier; ude.anozira.liame@sevy

Received 24 April 2017; Revised 26 June 2017; Accepted 30 July 2017; Published 30 August 2017

Academic Editor: Cui Tao

Copyright © 2017 Jung-wei Fan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Exposome 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.