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. 2Health-e-Child Establish Horizontal and Vertical integration of 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 Knowledge RepositoryPaediatrics A Knowledge Repository for Paediatrics Establish Horizontal and Vertical integration of 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 Knowledge RepositoryPaediatrics A Knowledge Repository for Paediatrics Cardiology Tetralogy of Fallot (ToF) Cardiomyopathy (HCM, DCM) Rheumatology Juvenile Idiopathic Arthritis (JIA) NeuroOncology Brain Tumors – Gliomas

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

4. 4Health-e-Child  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 Health-e-Child Europe-wide Information Platform for Pediatrics

5. 5Health-e-Child Research Focus in Rheumatology WristHip 163 patients enrolled (Target – 300) 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 INternational Trials Organization

6. 6Health-e-Child 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 257(RVO)+39(CMP) patients enrolled (Target – 300) Short AxisLong Axis

7. 7Health-e-Child 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. 8Health-e-Child Vertical Data Integration

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

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

11. 11Health-e-Child Data Import into HeC Migration tool imports XML forms created by Siemens data collection tool UMLSTool 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. 12Health-e-Child DistributionDistribution transaction IGG GOSHNECKER AccessPointAccessPoint HeC Gateway ++ + ICD Integrated Case Database (ICD) -Grid Database of Patient Data -From clinical records to files -Distributed (1 per Hospital) -Multi-centre (federation) -Fine-grained Access Controls -Synced with VO new VO  AMGA sync daemon new VO  AMGA sync daemon -ACLs until records Integrated Case Database (ICD) -Grid Database of Patient Data -From clinical records to files -Distributed (1 per Hospital) -Multi-centre (federation) -Fine-grained Access Controls -Synced with VO new VO  AMGA sync daemon new VO  AMGA sync daemon -ACLs until records Data Overview -Database Abstraction AMGA -Database Backend Abstraction (AMGA Layer) -Transactional -Transactional insertion and updates -Replication -Replication of portions of the data for ISD and ICD v1 -Multi-levelIDM -Multi-level Integrated Data Model (IDM) -From Organs, to Cells, to Genes… -Medical Images along with clinical records -Multi-centreICD -Multi-centre Case Database (ICD) -ICDs are federated and seen as a single one -Patient privacy -Patient privacy is ensured from the beginning -Anonymisation client-side -UUIDs for all patient folders -Peer-To-Peer Patient Privacy -Peer-To-Peer Patient Privacy for storing mappings -Useful for retrieving concerned sets of patients

13. 13Health-e-Child Exploiting Integrated Data CaseReasoner Application Cardiac Example

14. 14Health-e-Child 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. 15Health-e-Child DistributionDistribution Similarity Distance Calculation IGG GOSHNECKER AccessPointAccessPoint HeC Gateway ++ + Process: 1. 1. Query for RV Meshes in ICD 2. 2. Process Similarity Distance Measurement « where data is » 3. 3. Aggregate results in a WEKA dataset 4. 4. Display result using Treemaps, NG graph or Heatmapper

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

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

18. 18Health-e-Child Virtual Volume Reduction Surgery

19. 19Health-e-Child Cross-Project Interoperability Health-e-LINK Application Data Mining Example

20. 20Health-e-Child Health-e-Child 3D Knowledge Browser

21. 21Health-e-Child Integration of @neurLINK from @neurIST