https://wiki.stamen.health/index.php?action=history&feed=atom&title=Knowledge_Graphs_in_HealthKnowledge Graphs in Health - Revision history2026-04-14T14:50:52ZRevision history for this page on the wikiMediaWiki 1.42.7https://wiki.stamen.health/index.php?title=Knowledge_Graphs_in_Health&diff=122&oldid=prevAdmin 3julmthh: Comprehensive update with worldwide projects and technologies2026-04-13T17:33:37Z<p>Comprehensive update with worldwide projects and technologies</p>
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= Knowledge Graphs in Health =<br />
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The use of graph databases, semantic technologies, and knowledge graphs for health data integration, clinical decision support, and longitudinal patient records.<br />
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== Overview ==<br />
Knowledge graphs are increasingly recognized as a foundational technology for healthcare AI. Neo4j's research shows "healthcare leads in knowledge graphs" and CIOs "can't afford to scale AI without a knowledge graph foundation."<ref>Neo4j (2025): "Why Healthcare CIOs Can't Afford to Scale AI Without a Knowledge Graph Foundation" — neo4j.com</ref> Towards Data Science notes that healthcare leads other industries in knowledge graph adoption.<ref>Towards Data Science: "Why Healthcare Leads in Knowledge Graphs"</ref><br />
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== Key Projects ==<br />
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=== AIDAVA (EU) ===<br />
[[AIDAVA]] — €7.7M Horizon Europe project (2022–2026) creating a virtual assistant for automated curation and publishing of personal health data. Uses Personal Health Knowledge Graphs (PHKG) based on SNOMED, FHIR, and LOINC ontologies. 13 partners from 9 countries. Coordinator: [[Maastricht University]]. Knowledge graph technology by [[Ontotext]].<ref>aidava.eu / OpenAIRE grant 101057062</ref><br />
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=== AllegroGraph (Franz Inc.) ===<br />
Franz Inc.'s '''AllegroGraph''' is a semantic graph database widely used in life sciences and healthcare. Supports RDF, SPARQL, and knowledge graph applications for clinical data integration and linked health data.<ref>Franz Inc. — franz.com/agraph</ref><br />
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=== Neo4j Health Applications ===<br />
Neo4j, the leading graph database, has published extensively on healthcare knowledge graphs including '''MedQGraph''' (Temporal Medical Knowledge Graphs for AI-Driven Healthcare Insights) and clinical data integration patterns.<ref>Neo4j — neo4j.com, MedQGraph</ref><br />
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=== GraphDB (Ontotext) ===<br />
'''GraphDB''' by [[Ontotext]] (now part of Graphwise) is an enterprise knowledge graph database used in health data integration. Powers the knowledge graph backbone of AIDAVA.<ref>PR Newswire (2024): "Semantic Web Company and Ontotext Merge to Create Knowledge Graph and AI Powerhouse Graphwise"</ref><br />
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=== OHDSI / OMOP Common Data Model ===<br />
The '''OHDSI''' (Observational Health Data Sciences and Informatics) community uses the '''OMOP Common Data Model''' with graph-based approaches for observational health research. Recent work includes graph visualization and validation of drug mappings using LLMs.<ref>medRxiv (2025): "Rx Norm for Europe — Toward the representation of medicinal products in the OMOP CDM"</ref><br />
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=== PubMed Knowledge Graph 2.0 ===<br />
Connects papers, patents, and clinical trials in biomedical science using graph structures.<ref>Nature (2025): "PubMed knowledge graph 2.0"</ref><br />
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=== i-HD Projects ===<br />
[[i-HD]] participates in multiple projects using knowledge graph approaches for health data, including IDERHA (integration of heterogeneous data), QUANTUM (data quality labels), and SYNTHEMA (synthetic data generation).<ref>i-hd.eu/projects</ref><br />
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=== LLM + Knowledge Graphs ===<br />
Large language models are increasingly used to construct and query health knowledge graphs. Nature published on "Large language model powered knowledge graph construction for mental health exploration."<ref>Nature (2025)</ref> Frontiers published on "Unlocking electronic health records: a hybrid graph RAG approach to safe clinical AI."<ref>Frontiers (2025)</ref><br />
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== Technology Stack ==<br />
Common technologies used in health knowledge graphs:<br />
* '''Graph Databases''': Neo4j, GraphDB (Ontotext), AllegroGraph (Franz Inc.), Amazon Neptune<br />
* '''Standards''': RDF, OWL, SPARQL, SHACL<br />
* '''Health Ontologies''': SNOMED CT, LOINC, HL7 FHIR, ICD-10, OMOP CDM<br />
* '''Integration''': ETL pipelines, FHIR APIs, NLP extractors<br />
* '''AI/ML''': Graph neural networks, LLMs for KG construction, embedding models<br />
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== See Also ==<br />
* [[AIDAVA]]<br />
* [[Interoperability]]<br />
* [[Longitudinal Journals]]<br />
* [[Ontotext]]<br />
* [[i-HD]]<br />
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<references /><br />
[[Category:Topic]]</div>Admin 3julmthh