Time-Related Patient Data Retrieval for the Case Studies from the Pharmacogenomics Research Network
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There are lots of question-based data elements from the pharmacogenomics research network (PGRN) studies. Many data elements contain temporal information. To semantically represent these elements so that they can be machine processiable is a challenging problem for the following reasons: (1) the designers of these studies usually do not have the knowledge of any computer modeling and query languages, so that the original data elements usually are represented in spreadsheets in human languages; and (2) the time aspects in these data elements can be too complex to be represented faithfully in a machine-understandable way. In this paper, we introduce our efforts on representing these data elements using semantic web technologies. We have developed an ontology, CNTRO, for representing clinical events and their temporal relations in the web ontology language (OWL). Here we use CNTRO to represent the time aspects in the data elements. We have evaluated 720 time-related data elements from PGRN studies. We adapted and extended the knowledge representation requirements for EliXR-TIME to categorize our data elements. A CNTRO-based SPARQL query builder has been developed to customize users’ own SPARQL queries for each knowledge representation requirement. The SPARQL query builder has been evaluated with a simulated EHR triple store to ensure its functionalities.
KeywordsTemporal ontology Semantic web Pharmacogenomics studies SPARQL query builder
This work was partially supported by the NIH/NIGMS (U19 GM61388; the Pharmacogenomic Research Network, PGRN) and the Strategic Health IT Advanced Research Projects (SHARP) Program (90TR002) administered by the Office of the National Coordinator for Health Information Technology. The contents of the manuscript are solely the responsibility of the authors.
Conflict of Interest
The authors declare that they have no conflict of interest.
- 3.Zhu, Q. F. R., Durski, M. J., Pathak, J., Chute, C. G., Standardization of Data Dictionaries: A Case Study from the Pharmacogenomics Research Network. AMIA Summit Clin Res. Inform., 2012.Google Scholar
- 4.Tao C, Solbrig HR, Sharma DK, Wei W-Q, Savova GK, Chute CG. Time-oriented question answering from clinical narratives using semantic-web techniques. Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II. Shanghai, China: Springer-Verlag; 2010. p. 241-56.Google Scholar
- 5.Tao, C., Wei, W. Q., Solbrig, H. R., Savova, G., and Chute, C. G., CNTRO: A Semantic Web Ontology for Temporal Relation Inferencing in Clinical Narratives. AMIA Annu. Symp. Proc. 2010:787–791, 2010.Google Scholar
- 6.UMLS Semantic Types, http://wwwnlmnihgov/research/umls/META3_current_semantic_typeshtml.
- 7.Zhu, Q. F. R., Lian, Z., Bauer, H. S., Durski, M. J., Tao, C., Pathak, J., Chute, C. G., Harmonization and semantic annotation of data dictionaries from the Pharmacogenomics Research Network: a case study. Submitted to Journal of Biomedical Informatics, 2012.Google Scholar
- 8.Boland, M.R., Tu, S.W., Carini, M.A., Sim, I., Weng, C., EliXR-TIME: A Temporal Knowledge Representation for Clinical Research Eligibility Criteria. AMIA Summits Transl. Sci. Proc., 2012.Google Scholar
- 10.A Semantic Web Rule Language Combining OWL and RuleML. [cited 02/23/2012]; Available from: http://www.w3.org/Submission/SWRL/.
- 12.Sesame, http://wwwopenrdforg/indexjsp.
- 13.VIVO, http://www.vivoweb.org.
- 14.DistilBio, http://distilbio.com/.
- 15.SPARQL Assistant, http://sadiframework.org/content/tag/sparql-assist/SPARQL Assist.
- 16.iSPARQL, http://dbpedia.org/isparql/.