the health-e-child project & platform data integration - semantic and syntactic interoperability
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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The Health-e-Child Project & PlatformData Integration - Semantic and Syntactic
Interoperability
David Manset – MAAT-G
March 5th, 2009
EGEE-UF/OGF25 Catania, Sicily
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2 Health-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
• A Knowledge Repository for Paediatrics
Cardiology•Tetralogy of Fallot (ToF)•Cardiomyopathy (HCM, DCM)
Rheumatology•Juvenile Idiopathic Arthritis (JIA)
NeuroOncology•Brain Tumors – Gliomas
Data IntegrationEnabling Tools
Knowledge DiscoveryDecision Support
Data Mining …
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3 Health-e-Child
The Grid
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 ScalabilityS
uper
Com
putin
gG
rid C
ompu
ting
HealthgridHealthgrid +
Storage Capacity• Storing Millions of Medical
Images and Clinical Records• Storing newly generated
Knowledge • Caching intermediary image
processing data
Computing Power• Data Mining, e.g. Image
Processing• Knowledge Discovery,
similarity searches over large populations of patients
Connectivity, Security• Sharing Data, Knowledge and Applications
cross-institution, cross-country• Securing Infrastructure and ensuring
patient privacy• Gluing heterogeneous Information
Systems• Abstracting from legacy technologies
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4 Health-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
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5 Health-e-Child
Research Focus in Rheumatology
Wrist Hip
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
progressionDevelop 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
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6 Health-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
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7 Health-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)
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8 Health-e-Child
Vertical Data Integration
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9 Health-e-Child
De-Identified Electronic Patient Record
• Siemens web based data collection tool• Adjusted for Health-e-Child
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10 Health-e-Child 10
Patient Study, Diagnosis, Therapy
Patient Information
Pedigree
Medical History
ICD
Data Import into HeC
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11 Health-e-Child
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
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12 Health-e-Child
DistributionDistribution
transaction
transaction
transaction
IGG GOSH NECKER
AccessPoint
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- ACLs until records
Data Overview
-Database Backend Abstraction (AMGA Layer)-Transactional insertion and updates-Replication of portions of the data for ISD and ICD v1
-Multi-level Integrated Data Model (IDM)- From Organs, to Cells, to Genes…- Medical Images along with clinical records
-Multi-centre Case Database (ICD)- ICDs are federated and seen as a single one
-Patient privacy is ensured from the beginning- Anonymisation client-side- UUIDs for all patient folders
-Peer-To-Peer Patient Privacy for storing mappings- Useful for retrieving concerned sets of patients
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13 Health-e-Child
Exploiting Integrated DataCaseReasoner Application
Cardiac Example
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14 Health-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
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15 Health-e-Child
DistributionDistribution
Similarity DistanceCalculation
IGG GOSH NECKER
AccessPoint
HeC Gateway
+ + +
Process:1. Query for RV Meshes in ICD2. Process Similarity Distance Measurement « where data is »3. Aggregate results in a WEKA dataset4. Display result using Treemaps, NG graph or Heatmapper
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16 Health-e-Child
Visualization of Result Set
• 3 specific non-traditional visualisation techniques• Treemaps [Shneiderman, 1992] (integration in progress)• Neighbourhood graphs [Toussaint, 1980]• Combined correlation plots/heatmaps [Verhaak, 2006]
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17 Health-e-Child
Step2: Electromechanical Model and Simulation
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 + fibresColors: strain anisotropy
Simulated beating heart + fibresColors: contraction
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18 Health-e-Child
Virtual Volume Reduction Surgery
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19 Health-e-Child
Cross-Project InteroperabilityHealth-e-LINK Application
Data Mining Example
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20 Health-e-Child
Health-e-Child 3D Knowledge Browser
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21 Health-e-Child
Integration of @neurLINK from @neurIST
Thank you for your attention!