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Who am I

Gianluca Correndo PhD student (end of PhD) Work in the group of medical informatics (Paolo Terenziani) PhD thesis on contextualization techniques for clinical guidelines Mostly devoted to flexible integration of ontologies in information systems. Who am I. Ontologies in Computer Science.

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Who am I

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  1. Gianluca Correndo PhD student (end of PhD) Work in the group of medical informatics (Paolo Terenziani) PhD thesis on contextualization techniques for clinical guidelines Mostly devoted to flexible integration of ontologies in information systems Who am I

  2. Ontologies in Computer Science • An ontology is a shared understanding of some domain of interest. [Uschold, gruninger96] • Defines • A common vocabulary of terms • Some specification (more or less formal) of the meaning of the terms • A shared understanding for people and machines

  3. Why develop an ontology? • To make domain assumptions explicit • Easier to change domain assumptions • Easier to understand and update legacy data • To separate domain knowledge from operational knowledge • Re-use domain and operational knowledge separately • A community reference for applications (standards) • To share a consistent understanding of what information means

  4. Syntax is not enough for machine communication, e.g. B2B Order information: <Product> <type>Car</type> <Name>Daimler 500 SLK </Name> <Price>23.000 $</Price> </Product> Bestellinformation: <Auto> <Name>Daimler 500 SLK </Name> <Preis>27.000 </Preis> </Auto> Communication

  5. Ontologies in Medical Domain • Clinical structured data capture and presentation - letting physicians enter, store, and review in a more structured way than free text notes. • Information integration, indexing and retrieval - linking clinical records, decision support, quality assurance, and other information (Data Mart). • Messaging between software systems - linking laboratory and Hospital Information Systems providing a fixed semantic to terms used in message context • Reporting - providing the official returns in whichever coding system is required …… mostly terminological use of term “ontology”

  6. If ECG is altered for patient then … Heterogeneity of Data Sources in Medical Domain If “ECG” is altered.. Electronic Medical Record • Different data schema • Different data dictionary E01.370.405.240 EKG electrocardiogram ECG

  7. Virtual Integration Architecture

  8. Distributed Information Systems Local Local Global Ontology Ontology Ontology Local Data Data Ontology source source Data Data Data source source source Data source Harmonize heterogeneous domain conceptualizations Global Ontology Standard Standard Ontology #1 Ontology #2 Local Local Local Local Local Local Ontology Ontology Ontology Ontology Ontology Ontology Data Data Data Data Data Data source source source source source source

  9. Multi Standard Architecture for Guideline Managers GL GL described described in UML in LOINC terms terms Guideline Manager UMLS LOINC Local Local Local Ontology Ontology Ontology Medical Medical Medical Record Record Record

  10. Automated Ontology Extraction Local Ontology Medical Record

  11. Multi Standard in Bioinformatics Query described in gene ontology Guideline Manager Gene Ontology Local Local Local Local Local Local Ontology Ontology Ontology Ontology Ontology Ontology Gene database 2 Gene database 1 Gene database 3 Gene database 4 Gene database 5 Gene database 6

  12. EcoCyc

  13. Gene Ontology http://www.geneontology.org • “a dynamic controlled vocabulary that can be applied to all eukaryotes” • Built by the community for the community. • Three organising principles: • Molecular function, Biological process, Cellular component • Isa and Part of taxonomy – but not good! • ~10,000 concepts • Lightweight ontology, Poor semantic rigour. Ok when small and used for annotation. Obstacle when large, evolving and used for mining. • GO, OBO

  14. Tools for Ontology Engineering • Editing: protégé, oiled … • Access API: Jena, legacy • Languages: XMI, OWL, RDF(S)

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