ExaMode: Building Multi-modal Knowledge Graph for Better Diagnostics
Sirma AI (Ontotext) will deliver a webinar for Examode through BDVA (https://www.big-data-value.eu/) on the topic of ExaMode: Building Multi-modal Knowledge Graph for Better Diagnostics
The selected time slot is 2PM (CEST) on July 2, 2020.
Behind the scenes of linking histopathological data and knowledge graphs: How to extract structured data from medical records? What is the key role of ontologies and thesauri in semantic data fusion? What are the steps from knowledge discovery and exploration to the medical professionals assistance?
The agenda includes:
- Objectives of ExaMode project
- Knowledge management of diagnosis related medical data using advanced text analytical technologies and knowledge graphs
- Demonstration of services:
- advanced text analytics for semantic data normalization of Electronic Health Record (EHR) extracts – clinical synopsis;
- semantic data fusion of extracted results with a referential knowledge graph built from relevant ontologies and thesauri (Mondo Disease Ontology, Disease Ontology, UMLS, SNOMED-CT,and others);
- visual graph analytics and exploration of semantically normalized cases in the context of the referential knowledge graph;
- graph similarity search for identification of similar medical cases The solution is implemented on top of Ontotext’s GraphDB, a highly scalable RDF triple store.