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Emerging Technologies

Emerging Technologies. Semantic Web and Data Integration This meeting will start at 5 min past the hour As a reminder, please place your phone on mute unless you are speaking. 23 Aug 2013. Emerging Technologies. Semantic Web and Data Integration. 23 Aug 2013. Meeting Agenda.

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Emerging Technologies

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  1. Emerging Technologies Semantic Web and Data Integration This meeting will start at 5 min past the hour As a reminder, please place your phone on mute unless you are speaking 23 Aug 2013

  2. Emerging Technologies Semantic Web and Data Integration 23 Aug 2013

  3. Meeting Agenda Representing CDISC Standards in RDF Update New Use Cases Overview Group I Use Cases Group II Use Cases Next Steps

  4. Representing CDISC Standards in RDF Phase I Deliverables: CDASH v1.1 including domain assumptions SDTM v1.2/SDTM IG v3.1.2 including domain assumptions SDTM v1.3/SDTM IG v3.1.3 including domain assumptions SEND IG v3.0 including domain assumptions ADaM v2.1/ADaM IG v1.0 Controlled Terminology for CDASH, SEND, and ADaM

  5. Representing CDISC Standards in RDF Phase I Status: NCI assumed ownership of Controlled Terminology RDFs Draft RDFs of CDISC models/IGs completed by three sub-teams Phase I completed

  6. Representing CDISC Standards in RDF Phase II: Restructure three model-based sub-teams into one Consolidate draft models into a final work package Configure GitHub code repository Publish consolidated, draft models on GitHub for public review Discuss and finalize review and ownership process with CDISC

  7. Group I (Sep 2013): Representing Regulations and Guidance in RDF – Mitra Representing CDISC Conformance Checks – Scott Representing SDTM, SEND, and ADaM Datasets in RDF – Phil Toolsets to Access Clinical Trial Data Represented in RDF (e.g. SAS, R, etc.) – Marc New Use Cases: Overview

  8. Group II (Oct/Nov 2013): Representing CDISC Protocol Representation Model in RDF – Geoff EHR Enabled Research – Landen New Use Cases: Overview

  9. Objective: Evaluate the feasibility of representing regulations and guidance documents in RDF Rationale: Various sources (e.g. CDISC standards) refer to regulations and guidance documents. Representing the regulations and guidance documents in RDF enables the linking of said documents to RDF sources Representing Regulations and Guidance Documents in RDF

  10. Deliverables: Identify regulations and guidance documents referenced in CDISC standards Evaluate the feasibility of representing regulations and guidance documents in RDF. If feasible, represent the regulations and guidance documents in RDF If feasible, link the RDF representations to CDISC standards represented in RDF Representing Regulations and Guidance Documents in RDF

  11. Objective: Represent SDTM, SEND, and ADaM validation rules in RDF Implementation algorithms will not be represented Rationale: ADaM standard includes validation rules Identifying all validation rules associated with a domain or variable is a non-trivial, manual task Vendor agnostic representation of SDTM and SEND validation rules Representing SDTM, SEND, and ADaM Validation Rules in RDF

  12. Deliverables: Define ontology for validation rules Identify version(s) of SDTM/ADaM validation rules for representation Represent SDTM/ADaM validation rules in RDF Link the RDF representations to CDISC standards represented in RDF Representing SDTM, SEND, and ADaM Validation Rules in RDF

  13. Objective: Represent SDTM and ADaM clinical trial data in RDF Rationale: Representing clinical trial data in RDF enables more efficient data integration Representing SDTM and ADaM Datasets in RDF

  14. Deliverables: Working example demonstrating the steps required to create SDTM and ADaM datasets in RDF Create Formal SDTM and ADaM ontologies (reusing and building upon meta models created in first use case) Data alignment process SAS/TSV -> Formal Ontologies -> RDF (data processing scripts) Enhancement of RDF incorporating linked data concepts (data processing scripts) Demonstration of capabilities that RDF provides Traversing data using links, querying the data, etc. Representing SDTM and ADaM Datasets in RDF

  15. Objective: Identify toolsets to access and analyze clinical trial data represented in RDF Rationale: Realizing the benefits of clinical trial data represented in RDF requires toolsets that can access and analyze the data Toolsets to Access and Analyze Clinical Trial Data Represented in RDF

  16. Deliverables: Document toolsets including methods for accessing and processing RDF data sources Demonstrate access methods using data provided by Clinical Trial Data sub-team Publish working examples to GitHub Toolsets to Access and Analyze Clinical Trial Data Represented in RDF

  17. Representing CDISC Protocol Representation Model (PRM) in RDF EHR Enabled Research IHE and CDISC developed an integration profile provide that allows for the retrieval of metadata from an ISO 11179 metadata repository. The profile can be used to retrieve data from an EHR export document using a metadata enriched eCRF Group II Use Cases

  18. Creation of sub-teams Contact the sub-team leads or indicate your interest on the PhUSE Wiki Sub-team kick-off meetings: 09Sep – 13Sep Group I Use Cases: Next Steps

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