Knowledge-data integration for temporal reasoning in a clinical trial system.

Area of Science:

• Clinical Research Informatics
• Data Management
• Ontology Engineering

Background:

• Managing time-stamped data in clinical research is complex, often hindered by the disconnect between database technologies and domain knowledge.
• Current clinical research systems struggle to adequately represent and integrate temporal data with essential domain expertise.
• A gap exists between how research data is stored in databases and the conceptual understanding of clinical research.

Purpose of the Study:

• To present methodologies for ontology-based specification of temporal information in clinical research.
• To apply these methodologies to verify temporal constraints within clinical trial activities.
• To bridge the gap between relational database data and high-level clinical research concepts.

Main Methods:

• Developed ontology-based methodologies for temporal information specification.
• Utilized Semantic Web languages, specifically Ontology Web Language (OWL) and Semantic Web Rule Language (SWRL).
• Applied these methods to evaluate knowledge-level temporal constraints against operational trial data in relational databases.

Main Results:

• Demonstrated the successful integration of low-level relational data with high-level domain concepts.
• Showcased how OWL and SWRL can support research data management tools.
• Enabled the verification of protocol-specific temporal constraints in clinical trials.

Conclusions:

• Ontology-based approaches effectively manage temporal data and domain knowledge in clinical research.
• The proposed methodologies enhance data management tools by integrating relational data with study design concepts.
• This approach facilitates robust verification of temporal constraints, improving clinical trial data integrity.