Abstract
BackgroundClinical Intelligence, as a research and engineering discipline, is dedicated to the development of tools for data analysis for the purposes of clinical research, surveillance, and effective health care management. Self-service ad hoc querying of clinical data is one desirable type of functionality. Since most of the data are currently stored in relational or similar form, ad hoc querying is problematic as it requires specialised technical skills and the knowledge of particular data schemas.ResultsA possible solution is semantic querying where the user formulates queries in terms of domain ontologies that are much easier to navigate and comprehend than data schemas. In this article, we are exploring the possibility of using SADI Semantic Web services for semantic querying of clinical data. We have developed a prototype of a semantic querying infrastructure for the surveillance of, and research on, hospital-acquired infections.ConclusionsOur results suggest that SADI can support ad-hoc, self-service, semantic queries of relational data in a Clinical Intelligence context. The use of SADI compares favourably with approaches based on declarative semantic mappings from data schemas to ontologies, such as query rewriting and RDFizing by materialisation, because it can easily cope with situations when (i) some computation is required to turn relational data into RDF or OWL, e.g., to implement temporal reasoning, or (ii) integration with external data sources is necessary.
Highlights
Clinical Intelligence, as a research and engineering discipline, is dedicated to the development of tools for data analysis for the purposes of clinical research, surveillance, and effective health care management
We describe a prototype based on the SADI technology, we created to experiment with semantic querying, and report the results of a case study performed for several scenarios related to the surveillance of, and research on hospital-acquired infections, using an extract from a hospital datawarehouse
We are developing the Hospital-Acquired Infections (HAI) Ontology (HAIO) [24,25] that defines a number of HAI-specific concepts, such as Surgical_site_infection and Hospitalacquired_tuberculosis, adds a small hierarchy of general health care-related concepts, such as Disease and Medical_test, and aligns the resulting ontology with a number of third party ontologies, both general and specialised
Summary
Clinical Intelligence, as a research and engineering discipline, is dedicated to the development of tools for data analysis for the purposes of clinical research, surveillance, and effective health care management. Since most of the data are currently stored in relational or similar form, ad hoc querying is problematic as it requires specialised technical skills and the knowledge of particular data schemas. Clinical intelligence and ad hoc querying of relational data Clinical Intelligence (CI) is essentially Business Intelligence applied to clinical data, i.e., it is a business vertical and a research and engineering field aimed at the development of methods and tools for deriving insights from clinical data, required for research, surveillance and rational health care management (see, e.g., [1,2,3,4,5] to get a flavour of different directions of CI work). Semantic querying: problem and existing approaches To be economical and accessible, ad hoc querying has to be self-service, so that non-technical users – clinical researchers, surveillance practitioners, health care managers, etc.
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