Mining Interesting Clinico-Genomic Associations: The HealthObs Approach

  • George Potamias
  • Lefteris Koumakis
  • Alexandros Kanterakis
  • Vassilis Moustakis
  • Dimitrsi Kafetzopoulos
  • Manolis Tsiknakis
Part of the IFIP The International Federation for Information Processing book series (IFIPAICT, volume 247)


HealthObs is an integrated (Java-based) environment targeting the seamless integration and intelligent processing of distributed and heterogeneous clinical and genomic data. Via the appropriate customization of standard medical and genomic data-models HealthObs achieves the semantic homogenization of remote clinical and gene-expression records, and their uniform XML-based representation. The system utilizes data-mining techniques (association rules mining) that operate on top of query-specific XML documents. Application of HealthObs on a real world breast-cancer clinico-genomic study demonstrates the utility and efficiency of the approach.


Association Rule Association Rule Mining Electronic Healthcare Record Semantic Homogenization Linked Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • George Potamias
    • 1
  • Lefteris Koumakis
    • 1
  • Alexandros Kanterakis
    • 1
  • Vassilis Moustakis
    • 1
    • 3
  • Dimitrsi Kafetzopoulos
    • 2
  • Manolis Tsiknakis
    • 1
  1. 1.Institute of Computer Science (ICS)Foundation for Research & Technology — Hellas (FORTH)Heraklion, CreteGreece
  2. 2.Institute of Molecular Biology & Biotechnology (IMBB)Foundation for Research & Technology — Hellas (FORTH)Heraklion, CreteGreece
  3. 3.Department of Production Engineering & ManagementTechnical University of CreteChania, CreteGreece

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