First Concertation Meeting BMI Workshop eHealth FP6

1. First Concertation MeetingBMI WorkshopeHealth FP6 NoE No. 507505 Semantic Interoperability and Data Mining in Biomedicine [SemanticMining] Coordinator Hans Åhlfeldt - Linköping University, Sweden SemanticMining No.507505

2. Application Areas... ...Research Areas • Knowledge engineering • Ontology engineering • Coding, indexing and information retrieval • Data mining, knowledge extraction and representation • Natural Language Processing • The Semantic Web • …to support application areas • Information and decision support • Infrastructure for health care information systems Health Statistics Health Care Bioinformatics

3. Applications Research focus Information Models NLP Ontologies • Network is about harmonising knowledge representation strategies and ontologies • Ontology and knowledge queries pervade all levels of semantic web and middleware architectures • Can’t take result of one query, and use it as argument in next query to a different service, if KR strategies and ontologies are inconsistent, or not compatible Domain Ontologies Decision Support Models SemanticMining No.507505

4. Integration • … to bridge gaps in the European research infrastructure and to facilitate cross-fertilisation between disciplines … • Computer science (computer linguistics, natural language processing etc.) [6 partners] • Bio- and medical informatics [11 partners] • Health care organisations, standardisation bodies [6 partners] • SMEs [2 partners] SemanticMining No.507505

5. Medium-sized NoE • 25 partners • 11 countries • ~ 100 researchers • ~ 35 PhD students SemanticMining No.507505

6. Partners N Short name Participant 1a LIU (IMT) Linköpings universitet, Dept Biomedical Engineering/Medical Informatics, Sweden 1b LIU (IDA) Linköpings universitet, Dept of Computer Science, Sweden 1c LIU (C-NPU) Linköpings universitet, C-NPU Int.Federation for Clinical Chemistry and Laboratory Medicine 3 KI Karolinska Institutet, Stockholm, Sweden 4 SU Västra Götalands läns landsting, Sahlgrenska University Hospital, Göteborg, Sweden 5 UGOT Göteborgs Universitet, Dept of Swedish, Sweden 6 UKLFR Universitätsklinikum Freiburg, Germany 7 UNIFR Albert-Ludwigs-Universität, Computer Linguistics, Freiburg, Germany 8 IFOMIS Universität Leipzig, Institute of Formal Ontology and Medical Information Science, Germany 9 CAU Christian-Albrechts-Universität zu Kiel, Institut Informatik und Praktische Mathematik, Germany 10 DIM Université de Genève, Medical Informatics Division, University Hospital of Geneva, Switzerland 11 UOM The Victoria University of Manchester, Dept Computer Science, UK 12 UCL University College London, Centre for Health Informatics and Multiprofessional Education, UK 13 ITRI University of Brighton, The Information Technology Research Institute, UK 14 INSERM Public Health and Medical Informatics Laboratory, UFR Broussais, Paris, France 15 CNR-ISTC Institute of Cognitive Science, Laboratory for Applied Ontology, Roma, Italy 16 EMBL-EBI European Molecular Biology Laboratory, European Bioinformatics Institute, UK 17 MEDINFO National Institute and Library for Health Information, Budapest, Hungary 18 NORDCLASS WHO Collaborating Centre for Classification of Diseases in the Nordic countries 19 SOS The National Board of Health and Welfare, Stockholm, Sweden 20 STAKES National Research and Development Centre for Welfare and Health, Finland 21 KITH KITH AS, Norwegian Centre for Medical Informatics, Norway 22 NBH The National Board of Health, Denmark 24 MRI Merrall-Ross International Ltd, Cheshire, UK25 EDSA European Dynamics, Greece SemanticMining No.507505

7. Budget first year SemanticMining No.507505

8. Types of activities SemanticMining No.507505

9. Research Areas • Ontology engineering (e.g. anatomy, GO) • SNOMED CT as reference terminology • Concept systems in laboratory medicine • Multi-lingual medical dictionaries • Data/text mining • The semantic-based electronic health record • Health statistics SemanticMining No.507505

10. Long-term Commitment • An evolutionary process into a self-sustainable network • Key issues for success … • linking to existing networks • creating a momentum • spreading of excellence • fostering partnership, sharing of PhD students, researchers, infrastructure • industrial and national support • long-term co-operation among partners, between universities and health care organisations will follow • successful research will generate new funding • Strategic planning • Network Integration Committee, Scientific Advisory Committee SemanticMining No.507505

11. Dissemination of Outcome • Contribution to standards (CEN TC251, HL7, W3C, ISO) • Educational material • Collaboration with public health care organisations • Disseminate to health care workers & the public • Open workshops and summer schools • Technology transfer • Benchmarks and contests (e.g. BioCreative, TREC Genomic Track) • Industrial liaison • Open source licensing (e.g. OpenEHR, OpenGALEN, CLEF) • Scientific reporting SemanticMining No.507505

12. Contribution to Standards • CEN / ISO • Gunnar Klein (TC 251 Chairman ) • Anders Thurin (Vocabulary for Terminological Systems Project leader ) • Magnus Fågelberg (European Terminology Group Convenor ) • Dipak Kalra (TC 251) • IUPAC • Urban Forsum (C-NPU, IFCC-IUPAC Chair) • HL7 • Dipak Kalra - Electronic Health Records • Jeremy Rogers, Alan Rector – vocabulary, terminology • W3C / Semantic Web / OWL • Robert Stevens, Jeremy Rogers • SWISS-PROT, Gene Ontology de facto standards • EBI, Midori Harris • OMG Life Sciences Research Domain Task Force • EBI SemanticMining No.507505

13. Management • Decision making • The Assembly (all members) • The Board (elected annually by Assembly) • Flexible steering • Committees, Work package groups, Annual budgeting • Communication • WP1-9, groupware WP3 • Management Office • Co-ordinator’s Office (Hans Åhlfeldt) • International Office Director • Legal Advisor (Göran Hessling) • Management Advisory Group SemanticMining No.507505

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15. Potential GRID Applications - • Data Mining • Text Mining and Information Retrieval • Image Processing N E E D S + SemanticMining No.507505

16. Data Mining • Machine Learning Approaches • Linear complexity (computer cheap): • naive Bayes, k-nearest neighbors • Quadratic complexity (computer expensive): • Support Vector Machine, Principal Component Analysis… •  Feature selection algorithms (chi-2, mutual information) might use GRID architecture for scalability SemanticMining No.507505

17. Data Mining and Information Retrieval • Text ~ Data Mining + Data Storage • Inverted Files grows exponentially with the size of the collection to be indexed • Needed to avoid expensive hardware solutions (Google) • Example • Indexing 500.000 MEDLINE abstracts = 3 weeks (using the easyIR tool) SemanticMining No.507505

18. Image Processing • Image ~ Data + Text + Storage Explosion • Improvement of peak performance • Performance needs are rare, so harware solutions are overkill • Example: • indexing 5000 radiology images (using GNU-GIFT) 5000 images = 40 h on a single machine • Three million pictures are stored in the PACS ! SemanticMining No.507505

19. Potential GRID Applications • GRID-based NLP techniques for information extraction on free text from clinical reports • high security and authentication requirements due to sensitive patient data • GRID-computing for large-scale lexical and domain knowledge acquisition from Web-based sources • GRID-computing for computer expensive knowledge and language processing applications (e.g. parsing, terminological reasoning) SemanticMining No.507505

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