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Real World Scenario: HealthAgents

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Real World Scenario: HealthAgents

  1. 1. Information Integration in HealthAgents Madalina Croitoru croitoru@lirmm.fr
  2. 2. Presentation Structure  HealthAgents: motivation and background  The HealthAgents Ontology: HaDo  Extensions – Representation: HaDom v.1  Extensions – Reasoning: HaDom v.2  Discussion
  3. 3. Participants •MicroArt •Universitat Autònoma de Barcelona •Universitat de València •ITACA •Pharma Quality Europe •Katholieke Universiteit Leuven •University of Edinburgh •University of Birmingham •University of Southampton
  4. 4. Objectives SCIENTIFIC Brain Tumour Classification New Pattern Recognition Methods New Candidate DB Checking “Self-learning” Classifiers “Trusted” Framework Dissemination TECHNOLOGICAL Large d-DWH Multi-Agent System New Ontology Auto-conversion of Tumour Data Tumour Exchange Protocol Classifiers & d-DWH Coupling Data Mart Comparison & Analysis Improved Classifiers Enhanced Security
  5. 5. Why brain tumours?  Clinical importance  Important cause of morbidity and mortality in adults and children  Few improvements in outcome  New approaches to management needed via greater understanding  Amenable to techniques  Brain is amenable to MR studies  Tissue available, surgery mainstay of treatment
  6. 6. Our solution
  7. 7. Architecture Maps relational database schema to HealthAgents ontological schema - D2R (SPARQL to RDBMS) Abstracts underlying database interaction from agent architecture - SPARQL vs RDQL Main control flow of agent. - Receive classifiers, retrieve data Access control, marshalling of data, track out going data, evaluation of reputation and trust of agents Communications - abstracted from rest of agent to allow flexibility in the underlying framework Relational Database Description of what the agent holds and what it is able to do - Can it classify? - Does it hold data?
  8. 8. Architecture Relational Database
  9. 9. Architecture
  10. 10. Architecture
  11. 11. Achitecture
  12. 12. Achitecture PROPOSITION: CLASSIFY CASE X [DATA ….] Agents: A B C D A B C D
  13. 13. Achitecture A B C D Agents: A B C D
  14. 14. Achitecture A B C D Agents: A B C D INFORMATION: CLASSIFICATION CASE X [Astrocytoma 3] INFORMATION: CLASSIFICATION CASE X [Astrocytoma 3] INFORMATION: CLASSIFICATION CASE X [Astrocytoma 2] INFORMATION: Insufficient Access Rights
  15. 15. HealthAgents: ontology
  16. 16. Two extremes We don’t want this We don’t want this either
  17. 17. What an ontology is and is not
  18. 18. Tower of Babel  Ontology is to facilitate mutual understanding  SW extends such mutual understanding to “machinery”  Machine readable  Machine processable and, at the same time  Human readable  Human understandable 早上好 ! Buenos Dias! Good Morning! Bonjour! おはよう ! Guten Morgen! Buona Mattina! Bom Dia! 좋은 아침 ! Καλημέρα!
  19. 19. Rumours about ontologies  Ontologies overly publicised:  “Ontology” is becoming a buzz word  “Ontology” can “say” whatever one wants to say  “Ontology” means inference  “Ontology” is the ultimate solution for interoperability
  20. 20. Ontology as a solution  A common language/vocabulary/terminology for various participants  Formalised in an unambiguous representation  For software agents, human experts, patients  A “static” conceptualisation of the world  “What is” rather than “How does”  Allows reasoning which respects the translation of concepts as sets of (possible) individuals  Provides the underlying knowledge model for other types of reasoning, e.g. Rule Based, Case Based, Bayesian Network, etc.  Temporal stamp can be used to introduce dynamic flavours  A standard template for information interchange
  21. 21. Why OWL (Web Ontology Language)?  Reasoning capability  subsumption relationship  Relies on necessary and sufficient definition of concepts  Good for maintaining a consistent ontology  W3C standard  Good support: existing systems and tools  Good compatibility:  many ontologies are developed in owl or will be translated into owl  Good extensibility
  22. 22. HaDO Version 0.1  A simple HealthAgents domain ontology is developed  Different imaging modalities  WHO (world health organisation) classification  Necessary information for creating a new case  Information to be passed to other agents and humans  Place-holder for new information in follow-up revisions
  23. 23. Introducing HaDOM  MRS: as the result of UAB&ITACA visit late 2006  Follow the HealthAgents database schema  Need to adapt according to the HA DB schema  MRI, MicroArray, and other examination modules  For completeness  No immediate use in HealthAgents?  Histopathology  Follow the WHO classification  Patient information  Minimum set for HealthAgents  Optimal amount need to be investigated
  24. 24. Working with HaDom 1/2  Mapping between the domain ontology and current HealthAgents DB schema  D2RQ mapping script (RR)  Driving querying HA-DB via D2RQ engine  Progressing well  DB schema specific D 2R Q DB1 DB3 DB2 DBn Onto
  25. 25. Working with HaDom 2/2  Using HaDom as the common language  Domain ontology contains only “invariants”  Only static domain knowledge  Dynamic and correlation knowledge can be built on top of HaDom  Inference rules can be built on top of domain ontology  First approach: using Conceptual Graphs
  26. 26.  Validation  visits to Birmingham will be arranged in August  DICOMpatibility  easiest solution: make concept and property names “dicomised”  Inference rules  Using HaDom for invariants  Describing dynamic knowledge with HaDom-vocabulary  Bayesian Network with HaDom compatible inputs and outputs  Role based access control with HaDom concepts as roles  Etc. What is missing: extensions
  27. 27. HADOM ontology – March 2008  The ontology provides the terminology for interoperability:  WHO tumour classes (2002, 2007)  HealthAgents core concepts:  MRS, MRI, HRMAS terminology  Patient, Clinical_Intervention, Medical_Control etc.  Security concepts
  28. 28. haCore: for basic concept hierarchies and necessary concepts classifier: gathers input and output parameters for HA classifiers WHO 2007 Classification WHO 2002 Classification 2007 2002
  29. 29. Conclusions  Presented our efforts in building the HealthAgents Ontology : HaDom  Knowledge Acquisition  Implementation  Evaluation  Revision  Knowledge Acquisition  Implementation  Evaluation… 
  30. 30. Lessons learnt…

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