Jovanovik, M., Milenkova, E., Jakubowski, M., & Hose, K. (2025). Towards Generating Synthetic EHR Knowledge Graphs – a Probabilistic Approach. In M. Dojchinovski & B. Spahiu (Eds.), Proceedings of the 1st GOBLIN Workshop on Knowledge Graph Technologies. https://doi.org/10.5281/zenodo.16912250
The 1st GOBLIN Workshop on Knowledge Graph Technologies (GOBLIN25)
en
Event date:
12-Jun-2025
-
Event place:
Leipzig, Germany
-
Number of Pages:
5
-
Peer reviewed:
Yes
-
Keywords:
Synthetic Data; Data Generation; Knowledge Graphs; Electronic Health Records; RDF
en
Abstract:
Advances in medical AI and data analytics require large amounts of patient data. Due to privacy concerns, such data is not always available. Synthetic data generation promises a solution to provide the required data despite privacy restrictions. In this paper, we therefore introduce SynMedRDF, an open-source tool to generate synthetic Electronic Health Records. It ensures clinical accuracy by using real-world probabilities and correlations. The data is output as an RDF knowledge graph, enabling structure- and semantics-aware sharing, linking, and analysis.
en
Project title:
Health virtual twins for the personalised management of stroke related to atrial fibrillation: 101136244 (European Commission)
-
Project (external):
GOBLIN COST Action Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, N. Macedonia
CC BY 4.0
