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Transforming Annotated Clinical Narratives into Pruned Interoperable Knowledge Graphs with SNOMED CT
Amila Kugic1, Markus Kreuzthaler1, Stefan Schulz1
1Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
Introduction:
Clinical narratives are difficult to process due to unstructured text, abbreviations, and jargon, which limit semantic interoperability. Converting them into knowledge graphs (KGs) and pruning SNOMED CT enables focused, interoperable representations without losing essential information.
Methods:
KGs were built from the n2c2 2019 dataset using SNOMED CT and UMLS to model concept relationships. Subgraphs were merged, redundancies removed, and interoperability enforced via mappings to ICD-10-CM, RxNorm, and LOINC. Non-interoperable or disconnected nodes were pruned, and experts evaluated pruning quality.
Results:
Radical pruning improved interoperability but often removed contextual detail. Context-aware pruning preserved clinically meaningful structures while producing compact, semantically rich KGs suitable for downstream NLP tasks.
Conclusion:
The approach enables interoperable, pruned KGs from clinical narratives, with future work focusing on hybrid pruning and clinical NLP applications.
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