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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

A model-driven approach for representing clinical archetypes for Semantic Web environments.

Catalina Martínez-Costa¹, Marcos Menárguez-Tortosa, Jesualdo Tomás Fernández-Breis

¹Departamento de Informática y Sistemas, Facultad de Informática, Universidad de Murcia, Campus de Espinardo, CP 30100 Murcia, Spain.

Journal of Biomedical Informatics

|July 2, 2008

Summary

Electronic Health Records (EHR) face challenges with incompatible systems. This study proposes using Semantic Web technologies and OWL to represent clinical archetypes, improving EHR interoperability and overcoming ADL limitations.

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Area of Science:

Health Informatics
Computer Science
Semantic Web Technologies

Background:

Electronic Health Records (EHR) aggregate lifelong clinical data.
Current EHR systems often use heterogeneous, incompatible data formats.
Archetypes, specified using Archetype Definition Language (ADL), are used in advanced EHRs but have semantic limitations.

Purpose of the Study:

To explore the use of Semantic Web technologies for specifying clinical archetypes in advanced EHR architectures.
To address the drawbacks of ADL in Semantic Web environments.
To propose a method for transforming ADL into OWL for the CEN EN13606 EHR architecture.

Main Methods:

Utilizing Semantic Web technologies (Ontology Web Language - OWL) to define clinical archetypes.
Comparing the advantages of OWL over ADL for semantic activities.
Developing a solution combining Semantic Web and Model-driven Engineering to convert ADL to OWL.

Main Results:

Demonstrated the suitability of OWL for representing clinical archetypes.
Highlighted the semantic advantages of OWL compared to ADL.
Proposed a transformation process from ADL to OWL.

Conclusions:

Semantic Web technologies, particularly OWL, offer a robust approach for clinical archetype specification in EHRs.
The proposed ADL to OWL transformation facilitates enhanced semantic interoperability within EHR architectures.
This work advances the integration of Semantic Web principles into healthcare informatics for improved data exchange and analysis.