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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
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From Clinical Information Systems to Personalized Health Knowledge Graphs
Sareh Aghaei1, Remzi Celebi2, Markus Kreuzthaler1
1Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
Studies in Health Technology and Informatics
|August 23, 2024
Summary
This study introduces a knowledge graph approach for electronic health records, enabling AI-driven insights and visualizations. This method ensures international interoperability using established semantic standards.
Area of Science:
- Computer Science
- Medical Informatics
Background:
- Electronic health records (EHRs) contain complex data.
- Standardized representation is crucial for interoperability and advanced analytics.
Purpose of the Study:
- To present a versatile solution for formally representing EHR content.
- To leverage knowledge graph and semantic web standards for EHR data.
Main Methods:
- Utilizing the knowledge graph paradigm.
- Employing Resource Description Framework (RDF) and Web Ontology Language (OWL).
- Integrating SNOMED CT and Fast Healthcare Interoperability Resources (FHIR) standards.
Main Results:
- A graph-based EHR representation was developed.
- The solution supports diverse data visualizations.
- Enabled AI-powered services like fuzzy retrieval and summarization.
Conclusions:
- The proposed knowledge graph model offers a flexible and interoperable approach to EHR data.
- This representation facilitates advanced data analysis and AI applications in healthcare.
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