The SPHN Schema Forge - transform healthcare semantics from human-readable to machine-readable by leveraging semantic...

Area of Science:

• Health Informatics
• Semantic Web Technologies
• Medical Knowledge Graphs

Background:

• The Swiss Personalized Health Network (SPHN) utilizes Resource Description Framework (RDF) for healthcare data encoding.
• RDF enables machine readability but poses challenges for human understanding of medical knowledge graphs.
• Manual translation from tabular SPHN Datasets to RDF schemas is time-consuming and labor-intensive.

Purpose of the Study:

• To automate and streamline the translation process of healthcare data from tabular formats to RDF.
• To develop a user-friendly tool for generating RDF schemas and associated resources.

Main Methods:

• Development of the SPHN Schema Forge web service.
• Automated conversion of SPHN-compliant Dataset spreadsheets into RDF schemas.
• Generation of SHACL rules for data validation, HTML visualizations, and SPARQL queries.

Main Results:

• The SPHN Schema Forge successfully automates the conversion of tabular data to RDF schemas.
• The tool generates essential components for data validation, visualization, and analysis.
• Significant reduction in manual effort and time for schema generation.

Conclusions:

• The SPHN Schema Forge enhances efficiency in creating medical knowledge graphs.
• Researchers can now dedicate more time to data interpretation and analysis.
• The tool supports the SPHN's mission of advancing personalized healthcare through data interoperability.