A semantically-aided architecture for a web-based monitoring system for carotid atherosclerosis

Area of Science:

• Medical Informatics
• Biomedical Engineering
• Health Information Systems

Background:

• Carotid atherosclerosis diagnosis relies on diverse clinical data, often fragmented across disparate Health Information Systems (HIS).
• Lack of interoperability between HIS hinders comprehensive data acquisition for accurate diagnosis.
• Existing systems struggle to unify heterogeneous data sources effectively.

Purpose of the Study:

• To propose a semantically-aided architecture for a web-based monitoring system for carotid atherosclerosis.
• To enhance interoperability of HIS by gathering and unifying heterogeneous data using an ontology.
• To create a common interface for data access, facilitating a more complete diagnostic process.

Main Methods:

• Developed a web-based architecture utilizing an application ontology for carotid atherosclerosis.
• Integrated heterogeneous data sources through semantic representation and ontological reasoning.
• Employed SPARQL query rewriting and ontology-based data access services for information retrieval.

Main Results:

• The architecture successfully integrated imaging exams and clinical profiles of 233 patients.
• Physicians could execute complex queries, demonstrating expanded retrieval capabilities.
• Promising results were achieved in terms of interoperability and ontological data integration.

Conclusions:

• The proposed semantically-aided architecture significantly enhances HIS interoperability for carotid atherosclerosis management.
• Ontology-driven data integration facilitates comprehensive data access, supporting improved clinical diagnosis.
• The system offers expanded query and retrieval functionalities, aiding physicians in evaluating patient data.