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Towards a Web of Clinical Knowledge 
Experience gained from DoDTAPS Research Project 
Claude Nanjo 
December 2014 SemanticWeb.com
Overview 
•The healthcare challenge 
•Exposing clinical knowledge on the Web as RDF 
•Transforming clinical content –An example from the DoDTAPS project 
•Getting closer to this vision
The Challenge 
The US Healthcare System is highly heterogeneous. 
How do we converge?
The Rapid Growth of Health Knowledge 
Information will need to be sharable and machine-processableif it is to scale
Exposed and Richly Linked Knowledge …
… That is computable, Reusable, and Actionable 
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
This Ecosystem Already Exists! 
Computing
EXPERIENCE GAINED FROM DODAND VA RESEARCH PROJECTS
Our Focus Today 
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
TAPS RDF Pipeline 
CHCS 
Model Transformation 
ICD9 to SNOMED 
DX translation 
NLP parse of 
Med SIG into 
discrete data 
Name parse 
into discrete data 
Address parse 
into discrete data 
graph 
alignment 
Cluster 
Node 
Cluster 
Node 
Cluster 
Node 
Graph Federation 
Graph 
transform & 
distribution 
Hadoop 
csv 
Clients 
Graph Clients
RDF Pipeline Definition 
For more information on the RDF Pipeline Framework, please visit: 
http://rdfpipeline.org/
Deterministic Model Transformation: CHCS Address to FHIR Address
Non-Deterministic Model Transformation: From CHCS SIG to FHIR
Leveraging SPARQL for Terminology Alignment
Deterministic, Declarative, Reusable Transformation
WHAT’S NEXT 
A multi-pronged approach
Capture Knowledge Declaratively 
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
Why we need standards to guide clinical capture 
•RDF makes the processing of clinical information staight-forward. 
–Lots of tooling 
–It is a known semantic model with a number of supported representations (RDF/XML, Turtle, …) 
•RDF cannot solve all problems 
–Transformations are expensive even with RDF 
–Hard to maintain 
–Transformations are lossyand not always deterministic 
•Need standards to converge on 
–Standards that allows convergence towards a common clinical baseline. 
–Great strides being made in the standards world 
–Customization and localization increase TCO
A few encouraging developments 
•The adoption of FHIR (Fast Healthcare Interoperability Resources) –an implementer-friendly resource-oriented, REST-based clinical resource framework 
–http://www.hl7.org/implement/standards/fhir/summary.html 
•The RDF Semantic Interoperability SWG at HL7 
–Goal: To help expose HL7 standards using OWL/RDF 
–Discussion on an OWL ontology for FHIR and a FHIR RDF representation 
•Convergence of two existing communities 
–HL7 
–W3C Semantic Web for Health Care and Life Sciences Group 
–http://wiki.hl7.org/index.php?title=RDF_for_Semantic_Interoperability
Expose Resources and Reusable Logic 
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
Yosemite part-4 webinar-final
Yosemite part-4 webinar-final
Encouraging developments 
•ONC’s Clinical Quality Framework Initiative 
–The harmonization of CDS and Clinical Quality models through FHIR 
–CDS ontologies? 
•CDS Collaborative 
–Collaboration between the University of Utah (OpenCDS) and Cognitive Medical Systems (Socratic Grid) 
–Declarative rules to be exposed on the Web 
–Enable access to clinical decision support services that leverage the web and RDF
Knowledge Discovery 
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
Other prototypes from the TAPS project 
•Record linkage through FHIR normalization using RDF 
–CHCS and VISTA 
•Patient Cohort Classification System 
•PTSD Capacity Planner
Provide Support At the Point of Care 
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
The Ask 
•Help us get there, get involved in an initiative 
•Engage the community 
•Contribute to Open Linked Knowledge

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Yosemite part-4 webinar-final

  • 1. Towards a Web of Clinical Knowledge Experience gained from DoDTAPS Research Project Claude Nanjo December 2014 SemanticWeb.com
  • 2. Overview •The healthcare challenge •Exposing clinical knowledge on the Web as RDF •Transforming clinical content –An example from the DoDTAPS project •Getting closer to this vision
  • 3. The Challenge The US Healthcare System is highly heterogeneous. How do we converge?
  • 4. The Rapid Growth of Health Knowledge Information will need to be sharable and machine-processableif it is to scale
  • 5. Exposed and Richly Linked Knowledge …
  • 6. … That is computable, Reusable, and Actionable CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
  • 7. This Ecosystem Already Exists! Computing
  • 8. EXPERIENCE GAINED FROM DODAND VA RESEARCH PROJECTS
  • 9. Our Focus Today CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
  • 10. TAPS RDF Pipeline CHCS Model Transformation ICD9 to SNOMED DX translation NLP parse of Med SIG into discrete data Name parse into discrete data Address parse into discrete data graph alignment Cluster Node Cluster Node Cluster Node Graph Federation Graph transform & distribution Hadoop csv Clients Graph Clients
  • 11. RDF Pipeline Definition For more information on the RDF Pipeline Framework, please visit: http://rdfpipeline.org/
  • 12. Deterministic Model Transformation: CHCS Address to FHIR Address
  • 14. Leveraging SPARQL for Terminology Alignment
  • 16. WHAT’S NEXT A multi-pronged approach
  • 17. Capture Knowledge Declaratively CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
  • 18. Why we need standards to guide clinical capture •RDF makes the processing of clinical information staight-forward. –Lots of tooling –It is a known semantic model with a number of supported representations (RDF/XML, Turtle, …) •RDF cannot solve all problems –Transformations are expensive even with RDF –Hard to maintain –Transformations are lossyand not always deterministic •Need standards to converge on –Standards that allows convergence towards a common clinical baseline. –Great strides being made in the standards world –Customization and localization increase TCO
  • 19. A few encouraging developments •The adoption of FHIR (Fast Healthcare Interoperability Resources) –an implementer-friendly resource-oriented, REST-based clinical resource framework –http://www.hl7.org/implement/standards/fhir/summary.html •The RDF Semantic Interoperability SWG at HL7 –Goal: To help expose HL7 standards using OWL/RDF –Discussion on an OWL ontology for FHIR and a FHIR RDF representation •Convergence of two existing communities –HL7 –W3C Semantic Web for Health Care and Life Sciences Group –http://wiki.hl7.org/index.php?title=RDF_for_Semantic_Interoperability
  • 20. Expose Resources and Reusable Logic CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
  • 23. Encouraging developments •ONC’s Clinical Quality Framework Initiative –The harmonization of CDS and Clinical Quality models through FHIR –CDS ontologies? •CDS Collaborative –Collaboration between the University of Utah (OpenCDS) and Cognitive Medical Systems (Socratic Grid) –Declarative rules to be exposed on the Web –Enable access to clinical decision support services that leverage the web and RDF
  • 24. Knowledge Discovery CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
  • 25. Other prototypes from the TAPS project •Record linkage through FHIR normalization using RDF –CHCS and VISTA •Patient Cohort Classification System •PTSD Capacity Planner
  • 26. Provide Support At the Point of Care CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes
  • 27. The Ask •Help us get there, get involved in an initiative •Engage the community •Contribute to Open Linked Knowledge