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The Health-e-Child Project & Platform Data Integration - Semantic and Syntactic Interoperability

The Health-e-Child Project & Platform Data Integration - Semantic and Syntactic Interoperability. David Manset – MAAT-G. March 5th, 2009 EGEE-UF/OGF25 Catania, Sicily. Establish Horizontal and Vertical integration of data, information and knowledge for Paediatrics

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The Health-e-Child Project & Platform Data Integration - Semantic and Syntactic Interoperability

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  1. The Health-e-Child Project & Platform Data Integration - Semantic and Syntactic Interoperability David Manset – MAAT-G March 5th, 2009 EGEE-UF/OGF25 Catania, Sicily

  2. EstablishHorizontal and Vertical integrationof data, information and knowledge for Paediatrics • Develop a grid-based biomedical information platform, supported by sophisticated and robust search, optimisation, and matching techniques for heterogeneous information, • Build enabling tools and services that improve the quality of care and reduce its cost by increasing efficiency • Integrated disease models exploiting all available information levels • Database-guided decision support systems • Large-scale, cross-modality information fusion and data mining for knowledge discovery • A Knowledge RepositoryforPaediatrics Data Integration Enabling Tools KnowledgeDiscovery DecisionSupport Data Mining …

  3. + One big computer + Powerful - Expensive - Centralized - Limited Scalability SuperComputing World is moving from supercomputers to grid computing that for a fraction of the cost are able to deliver the same services… Healthgrid Several regular computers + Powerful + Cheap + DeCentralized + UnLimited Scalability Grid Computing The Grid

  4. Health-e-Child Europe-wide Information Platform for Pediatrics • Three peadiatric hospitals • Gaslini, Genoa, Italy • GOSH, London, UK • Necker, Paris, France • OPBG, Rome, Italy • Strong interdisciplinary team across • Countries and languages • Technical and clinical fields • Research on three peadiatric disease areas: • Arthritis • Cardiac Disorders • Brain Tumours

  5. Research Focus in Rheumatology Improve current classification of JIA subtypes Identify homogeneous groups of clinical features Find early predictors of poor outcome Identify sensitive markers of joint damage progression Develop MRI and US paediatric scoring system Joint space width varies with age – studies performed on adult are not applicable on children. Robust Information Fusion Pattern discovery in multimodal data, correlation between genomic, clinical and image data Rely on the collaboration with PRINTO: Pediatric Rheumatology INternationalTrials Organization Wrist Hip 163 patients enrolled (Target – 300)

  6. Research Focus in Cardiology Concentrating on Right Ventricular Overload and Cardiomyopathies Computational electromechanical models of the heart RVO monitoring and decision support based on similar cases – similarity search on complex, multimodal data Decision Support based on semi-automatic feature extraction from cardiac MR Health-e-Child CaseReasoner Visualizing integrated biomedical data for patient cohorts using treemaps and neighborhood graphs Long Axis Short Axis 257(RVO)+39(CMP) patients enrolled (Target – 300)

  7. Research Focus in Neuro-oncology: Glioma growth model: • Interpolating growth between two time instances • Using proliferation and diffusion of tumor cells • Including high speed of tumor invasion in white vs. grey matter Knowledge Discovery, Finding Prognostic Markers: • Classification of low vs. high grade • Sub-typing of pilocytic astrocytomas (e.g. regarding tumour site, age) • Regression analysis of factors (clinical, imaging, genetics) that affect treatment outcome • Prediction of prognosis (survival rate and quality of life) 49 Studies Collected (Target – 77)

  8. Vertical Data Integration

  9. De-Identified Electronic Patient Record • Siemens web based data collection tool • Adjusted for Health-e-Child

  10. Data Import into HeC Patient Information Medical History Patient Study, Diagnosis, Therapy Pedigree ICD 10

  11. Data Import into HeC Migration tool imports XML forms created by Siemens data collection tool Tool semi-automatically analyses forms and suggests name and type according to HeC meta data model and UMLS Tool instantiates HeC data model and migrates patient data using gateway API no need to know underlying data base management system After once establishing the mapping, patient data can be migrated to the HeC grid fully automatically

  12. + + + Access Point ICD Integrated Case Database (ICD) • GridDatabase of Patient Data • Fromclinical records to files • Distributed (1 per Hospital) • Multi-centre (federation) • Fine-grained Access Controls • Syncedwith VO • new VO  AMGA syncdaemon • ACLsuntil records Data Overview HeC Gateway Distribution transaction transaction transaction • Multi-levelIntegrated Data Model (IDM) • FromOrgans, toCells, to Genes… • MedicalImagesalongwithclinical records • Multi-centre Case Database (ICD) • ICDs are federated and seen as a single one • Patientprivacyisensuredfromthebeginning • Anonymisationclient-side • UUIDsforallpatient folders • Peer-To-Peer PatientPrivacyforstoringmappings • Usefulforretrievingconcerned sets of patients GOSH NECKER IGG • DatabaseBackendAbstraction(AMGALayer) • Transactionalinsertion and updates • Replicationof portions of the data for ISD and ICD v1

  13. ExploitingIntegrated Data CaseReasoner Application CardiacExample

  14. Step 1: Anatomical Model from Cardiac MR • Anatomical model of right ventricle (RV) created from HeC data (based on 30 isotropic volumes from Gosh) • Semi-automatic initialisation of model based on detection library from Siemens Corporate Research • Multi-sequence view for model editing  Fast, accurate 4D quantification of RV volumes (ES, ED) from which RV ejection fraction and further measurements can be easily derived Manual annotations in diastole and sysole HeC application for semi-automatic annotations

  15. + + + Process: 1.Query for RV Meshes in ICD 2.ProcessSimilarity Distance Measurement « where data is » 3.Aggregateresults in a WEKA dataset 4. Display resultusingTreemaps, NG graph or Heatmapper Access Point HeC Gateway Distribution Similarity Distance Calculation GOSH NECKER IGG

  16. Visualization of Result Set • 3 specific non-traditional visualisation techniques • Treemaps[Shneiderman, 1992] (integration in progress) • Neighbourhoodgraphs[Toussaint, 1980] • Combined correlation plots/heatmaps[Verhaak, 2006]

  17. Volumetric mesh at time 0 Simulated fibres (+60° on the endocardium to -60° on the epicardium) Visual adjustment of simulation(Segmentation / Simulation) Simulated beating heart + fibres Colors: contraction Simulated beating heart + fibres Colors: strain anisotropy Step2: Electromechanical Model and Simulation

  18. Virtual Volume Reduction Surgery

  19. Cross-Project Interoperability Health-e-LINK Application Data MiningExample

  20. Health-e-Child 3DKnowledge Browser

  21. Integration of @neurLINKfrom @neurIST Thank you for your attention!

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