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 06 Sep 2013

2. Emerging Technologies Semantic Web and Data Integration 06 Sep 2013

3. Meeting Agenda New Projects: Call for Volunteers Semantic Technology Initiatives - Lee Feigenbaum Reusing Medical Summaries for Enabling Clinical Research – Landen Bain

4. 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 Projects: Call for Volunteers

5. Objective: – Pre-Population of data collection sets from the medical summaries of an eligible patient group through the use of an ISO 11179 enabled semantic metadata repository through IHE DEX profile. Rationale: – Demonstrating that a semantic metadata registry can bridge the gaps between data elements used in clinical research and clinical care for extracting the required data for clinical research studies from the existing patient medical summaries Re-using medical summaries for enabling Clinical research

6. Background: – IHE DEX Profile: –EHRs contains data elements that can be used to populate research case report forms –DEX leverages the power of an ISO/IEC 11179 Metadata Registry standard to apply mappings earlier in the process, at the point of form design –DEX will create a customized map of the elements in a particular case report form to the corresponding elements in the EHR export. The metadata registry maintains the exact interactions between the research and healthcare data elements, and will provide an exact map by which data can be extracted from the pre-population data set Re-using medical summaries for enabling Clinical research RetrieveDataElementList [QRPH -43]  Metadata ConsumerMetadata Source RetrieveMetadata [QRPH -44]  DEX maps secondary use domain data to healthcare data elements

7. Background: – Semantic MDR Implementation from SALUS An implementation of the ISO/IEC 11179 Metadata Registries standard with Semantic Web technologies on top of Linked Data principles Allows to link the data elements defined by different authorities (like the HITSP C154 data sets and CDISC SDTM variables) through linked data principles Allows definition of extraction specifications between abstract data element definitions to physical content models (like CCD documents) Open source version of this MDR is already available at: https://github.com/srdc/semanticMDR. https://github.com/srdc/semanticMDR Screencasts available at : http://www.srdc.com.tr/projects/salus/blog/?p=181 This MDR also implements the DEX Profile, hence it allows retrieving extraction specifications of data elements for different content models. Re-using medical summaries for enabling Clinical research

8. Use case from SALUS Project: ROCHE use case in SALUS project –a pilot scenario to demonstrate that, the semantic MDR and DEX can enable ROCHE to semantically annotate the data set collection required for an observational study through CDISC SDTM variables, and then through SALUS it can seamlessly collect the required datasets from EHR Sources although the EHR Sources send anonymized medical summaries in HL7 CCD formats Re-using medical summaries for enabling Clinical research

9. Use case from SALUS Project (ROCHE Data Collection set): Re-using medical summaries for enabling Clinical research Patient id (Pseudonym) Sex Date of birth (or year of birth) Date of ACS event Date of ACS +30 days (Start date) History of type 2 diabetes (T2D) before start date (Y/N) Date of the first T2D diagnosis date ever Average HbA1C over the 12 months before start date (will be missing for most non diabetic pts) Average systolic BP over 12 months before start date Average diastolic BP over 12 months before start date History of hypertension before start date (Y/N) Last BMI before start date Last weight before start date Ever smoked before start date(Y/N)  Smoked within the last 3 months of start date(Y/N)  Taken Sulfonylurea anytime within 3 months before start date (Y/N)  Taken metformin anytime within 3 months before start date (Y/N)  Taken insulin anytime within 3 months before start date (Y/N)  Taken Thiazolidinediones (Glitazones) anytime within 3 months before startdate  (Y/N)  Taken other oral anti-diabetic drugs within 3 months before start date (Y/N)  Had a CHF before start date (Y/N)  Had a CHF after start date (Y/N)  Date of CHF after start date  Patient died any time after start date(Y/N)  Date of Death

10. Use case from SALUS Project: ROCHE defines the data set definitions by annotating them with SDTM variables (Through a GUI) SALUS MDR hosts HITSP Data sets, and also the mappings of these data sets from ASTM/HL7 CCD medical summaries –Through XPATHs –If the medical summary is in RDF, SPARQL is also possible SALUS MDR hosts the SDTM data sets and also maintains the links between HITSP data sets and SDTM variables by referencing to the unique URIs of CDISC2RDF datasets SALUS MDR also implements IHE DEX, hence when it is asked about the extraction specifications of an SDTM variable from an HL7 CCD document, it automatically follows the link between SDTM datasets and HITSP datasets, retrieves the extraction specification from the CCD document, and readily gives this to the Tool used at ROCHE side The tool uses these mapping specifications of individual data elements and builds extraction specifications to automatically retrieve the required data from the CCD document. Re-using medical summaries for enabling Clinical research

11. Deliverables: Importing SDTM to Semantic MDR Importing HITSP Data elements to Semantic MDR, and defining mapping specifications to ASTM/HL7 CCD Defining the mappings between SDTM variables and HITSP data elements ( Through skos:exact match, skos:closeMatch) for a subset required in demo Demonstrate a Data Collection Tool for an observational study to use the DEX profile and semantic MDR capabilities to collect data from medical summaries – We can check whether we can produce and process the annotated data collection set as “CDISC Protocol Representation Model (PRM) in RDF” – We can check whether we can produce the collected data sets in “RDF” as it is going to be done for “Trial Data representation in RDF” Publish working examples to GitHub Timeline: October 2013- February 2014 Re-using medical summaries for enabling Clinical research