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Related Experiment Video

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Published on: September 20, 2018

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.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

Context-aware pruning of clinical knowledge graphs (KGs) enhances semantic interoperability and preserves crucial details from unstructured clinical narratives, improving downstream natural language processing (NLP) tasks.

Keywords:

Electronic Health Records Natural Language Processing Terminology

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Area of Science:

Medical Informatics
Natural Language Processing
Knowledge Representation

Background:

Clinical narratives present challenges in processing due to unstructured text, abbreviations, and jargon, hindering semantic interoperability.
Knowledge graphs (KGs) offer a structured representation, but require optimization for clinical data.

Purpose of the Study:

To develop and evaluate a context-aware pruning method for clinical knowledge graphs derived from SNOMED CT.
To improve the semantic interoperability and reduce the complexity of clinical KGs while retaining essential information.

Main Methods:

Constructed KGs from the n2c2 2019 dataset using SNOMED CT and UMLS.
Merged subgraphs, removed redundancies, and enforced interoperability via mappings to ICD-10-CM, RxNorm, and LOINC.
Applied context-aware pruning to remove non-interoperable nodes and evaluated expert-validated pruning quality.

Main Results:

Radical pruning enhanced interoperability but led to loss of contextual detail.
Context-aware pruning successfully preserved clinically meaningful structures.
The resulting KGs were compact, semantically rich, and suitable for downstream NLP tasks.

Conclusions:

Context-aware pruning is an effective strategy for creating interoperable and informative clinical KGs from unstructured narratives.
This approach facilitates focused representations for clinical NLP applications.
Future research will explore hybrid pruning techniques and direct clinical NLP applications.