A SEMANTIC WEB FRAMEWORK FOR REPRESENTING, LINKING AND ANALYZING MEDICAL DATA FOR OPTIMIZING LABORATORY UTILIZATION
Subbu, Suria Kala
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In this thesis, we investigate semantic web based methods for representing, linking and analyzing medical data. The main challenge addressed in this work is the transformation of data stored in a relational database to an ontological model that allows to represent as RDF triples and to link the data with external data sources using linked data principles. We have implemented a semantic analytics framework that comprises the following elements: (a) Domain-specific ontology to represent the data model and data inference. (b) RDMS data extraction using a domain-specific ontology (TBOX) based on the relational database schema; (c) Ontology instantiation (ABOX) that involves converting the relational data in terms of RDF triples. A key feature of our approach is the data is not physically migrated from the RDBS to RDF, rather we dynamically materialize the RDF triples thus avoiding the creation of a large RDF dump; (d) Linking the RDF data with available open data in RDF format using ontology-based concept alignments; and (e) Semantic analytics using SPARQL to identify semantically-salient patterns within the data. We have applied our semantic analytics data to analyze pathology lab data (over 5 years of pathology order data), where we were able to identify prevalent order-sets inherent within the data, and we also evaluated the change in the frequent order-sets over a five year time period.