Modelling digital health data: The ExaMode ontology for computational pathology

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Authors

Laura Menotti · University of Padua

Gianmaria Silvello · University of Padua

Manfredo Atzori · HES-SO University of Applied Sciences and Arts Western Switzerland

Svetla Boytcheva · Sirma Group (Bulgaria)

Francesco Ciompi · Sirma Group (Bulgaria)

Giorgio Maria Di Nunzio · Radboud University Nijmegen

Filippo Fraggetta

Abstract

The ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries.

Topics & Keywords

Biomedical Text Mining and Ontologies Cancer Genomics and Diagnostics AI in cancer detection

UN Sustainable Development Goals

Good health and well-being

Publication Details

Published in: Journal of Pathology Informatics

Volume 14, pp. 100332-100332

DOI: 10.1016/j.jpi.2023.100332

Field-Weighted Citation Impact: 0.61