@conference {W05-02, title = {W05-02: Therapeutic Indications and Other Use-case-driven Updates in the Drug Ontology}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {The Drug Ontology (DrOn) is a an OWL2-based representation of drug products and their ingredients, mechanisms of action, strengths, and dose forms, as well as of packaged drug products as represented by United States National Drug Codes (NDCs) [1-3]. The primary goal of DrOn is to support analyses of large, drug-related datasets such as pharmacy claims and EHR data.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {William R. Hogan and Josh Hanna and Amanda Hicks and Samira Amirova and Baxter Bramblett and Matthew Diller and Rodel Enderez and Timothy Modzelewski and Mirela Vasconcelos and Chris Delcher} } @conference {IT705, title = {IT705: A Realist Representation of Social Identity Data}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Social identities merit special treatment in realist ontologies. Their ontological status is unsettled, so we should model them in a manner that is agnostic with respect to their ontological status. Nevertheless, there is a clear criterion for determining whether a specific person has a particular identity, namely, whether that person asserts that they do. This social act forms the basis for a realist representation, not of social identities themselves, but of data about social identities. We report the representation of social identities in the Ontology of Medically Related Social Entities and show that it supports data integration and retrieval.

}, url = {http://ceur-ws.org/Vol-1747/IT705_ICBO2016.pdf}, author = {Amanda Hicks} } @conference {IT602, title = {IT602: A Semantic Web Representation of Entire Populations}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Accurately representing demographic realities is a critical component in creating useful, agent-based epidemiological models of infectious disease. Synthetic ecosystems are generated from Census data microsamples in a statistically-sound manner to maintain population-level demographic characteristics. These highly detailed representations of populations are the basis of many advanced simulations of infectious disease epidemics. Creating a standard, machine-readable representation of synthetic ecosystem data would enable easier use and integration with epidemic simulator software. Here we describe an ontology-based representation in Resource Description Framework (RDF) and Web Ontology Language (OWL) of version 1.0 of the 2010 U.S. Synthetic Population database by RTI International. Our representation draws upon applicable classes from several reference ontologies, including the Ontology of Medically Related Social Entities (OMRSE). After failing to find suitable ontological representations of several key data elements in the Synthetic Population dataset, we created new classes in OMRSE for representing employment status, employee roles, workplaces, residences, households, and age measurements. We loaded a test RDF dataset (structured according to ontologies in OWL) of synthetic individuals into a commercial triple store (Stardog) and validated the representation with SPARQL queries.

}, url = {http://ceur-ws.org/Vol-1747/IT602_ICBO2016.pdf}, author = {Daniel Welch and Amanda Hicks and Josh Hanna and William Hogan} } @conference {IT504, title = {IT504: OOSTT: a Resource for Analyzing the Organizational Structures of Trauma Centers and Trauma Systems}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Organizational structures of healthcare organiza-tions has increasingly become a focus of medical research. In the CAF{\'E} project we aim to provide a web-service enabling ontol-ogy-driven comparison of the organizational characteristics of trauma centers and trauma systems. Trauma remains one of the biggest challenges to healthcare systems worldwide. Research has demonstrated that coordinated efforts like trauma systems and trauma centers are key components of addressing this chal-lenge. Evaluation and comparison of these organizations is es-sential. However, this research challenge is frequently com-pounded by the lack of a shared terminology and the lack of ef-fective information technology solutions for assessing and com-paring these organizations. In this paper we present the Ontol-ogy of Organizational Structures of Trauma systems and Trauma centers (OOSTT) that provides the ontological founda-tion to CAF{\'E}{\textquoteright}s web-based questionnaire infrastructure. We present the usage of the ontology in relation to the questionnaire and provide the methods that were used to create the ontology.

}, url = {http://ceur-ws.org/Vol-1747/IT504_ICBO2016.pdf}, author = {Joseph Utecht and John Judkins and Terra Colvin Jr. and J. Neil Otte and Nicholas Rogers and Robert Rose and Maria Alvi and Amanda Hicks and Jane Ball and Stephen M. Bowman and Robert T. Maxson and Rosemary Nabaweesi and Rohit Pradhan and Nels D. Sanddal and M. Eduard Tudoreanu and Robert Winchell and Mathias Brochhausen} }