Semantic querying of relational data for clinical intelligence: a semantic web services-based approach

Area of Science:

• Clinical Intelligence
• Semantic Web Services
• Bioinformatics

Background:

• Clinical Intelligence focuses on developing tools for clinical research, surveillance, and healthcare management.
• Self-service ad hoc querying of clinical data is highly desirable but challenging due to complex relational database schemas.
• Existing methods require specialized technical skills and schema knowledge, limiting data accessibility.

Purpose of the Study:

• To explore the use of SADI Semantic Web services for semantic querying of clinical data.
• To develop a prototype infrastructure for semantic querying of clinical data.
• To facilitate surveillance and research on hospital-acquired infections.

Main Methods:

• Developed a prototype semantic querying infrastructure.
• Utilized SADI Semantic Web services for data querying.
• Focused on enabling semantic querying of relational clinical data.

Main Results:

• Demonstrated that SADI can support ad-hoc, self-service, semantic queries on relational data within a Clinical Intelligence context.
• The SADI approach proved effective for querying clinical data, including situations requiring computation or external data integration.
• Successfully developed a prototype for hospital-acquired infection surveillance and research.

Conclusions:

• SADI Semantic Web services offer a viable solution for semantic querying of relational clinical data.
• This approach enhances accessibility for clinical research and surveillance by simplifying data querying.
• SADI provides advantages over traditional methods, particularly when dealing with data transformation and integration challenges.