The Role of Ontologies for Sustainable, Semantically Interoperable and Trustworthy EHR Solutions
Structure of the talk
how to bridge this gap. . . ? finding reports progress notes discharge summaries terminologies classifications ontologies information models
Semantic enrichment of text using natural language technologies
Semantic enrichment of text using Natural Language Technologies How to correctly extract information from medical texts How to find an interoperable semantic representation formalism
Semantic enrichment of text using Natural Language Technologies How to correctly extract information from medical texts How to find an interoperable semantic representation formalism
Semantic enrichment of text using Natural Language Technologies How to correctly extract information from medical texts How to find an interoperable semantic representation formalism
Structure of the talk
Semantic enrichment: technical challenges
From narrative to structured content
Manual semantic enrichment
Manual semantic enrichment
Automated semantic enrichment
Document retrieval scenario
Document retrieval scenario
Document retrieval scenario
Term retrieval scenario
Term retrieval scenario 1 5 3 7 2 4
Information extraction: example
Pipeline for NLP Analysis
Possible outputs of sophisticated language processing text “understanding” is still the holy grail of computational linguistics
Specific challenges for medical language processing
Semantic enrichment of text using Natural Language Technologies How to correctly extract information from medical texts How to find an interoperable semantic representation formalism
Example
Counterexample (more realistic)
Example: codes and contexts
Why ontologies are not enough
Boundary problem Ontology Epistemic context
Ontology vs. Epistemology Ontology Epistemic context
Information models mix ontological with epistemic information yes no don’t know
Outlook
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