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Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
Semantic Web use cases in outcomes research
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Semantic Web use cases in outcomes research

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Presentation given to University of Akron graduate CS class about work done with semantic web and outcomes research

Presentation given to University of Akron graduate CS class about work done with semantic web and outcomes research

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  • 1. http://metacognition.info/presentations/SW-usecases-outcomes-research.ppt Semantic Web use cases in outcomes research Experiences from building a patient repository and developing standards Chimezie Ogbuji Metacognition Inc. (Owner)
  • 2. Outline• Me• Semantic Web and Semantic Web technologies • RDF, GRDDL, OWL, RIF, and SPARQL• Cleveland Clinic Semantic DB project • Content repository • Data collection workflow • Quality and outcomes reporting • Cohort identification• Use of the system
  • 3. Me and the Semantic Web• I’ve been developing software using standards of the Semantic Web since 2001 • Worked on a startup that developed an XML & RDF content repository• Began working on Cleveland Clinic SemanticDB project in 2003• Began working in the World-Wide Consortium (W3C), developing the SPARQL and GRDDL standards in 2007 and 2006, respectively• I contribute to and maintain several open source software projects related to Semantic Web technologies: • RDFLib (https://code.google.com/p/rdflib/) • FuXi (https://code.google.com/p/fuxi/) • Akamu (https://code.google.com/p/akamu/)
  • 4. The Semantic Web• The Semantic Web • What is it? Like asking “What is the Matrix?” • A vision of how the existing WWW can be extended such that machines can interpret the meaning of data involved in protocol interactions • A vision of the founder of the World-wide Web Consortium (W3C) and inventor of the internet (Tim Berners-Lee)• Semantic Web technologies / standards • Layers of W3C standards (“Layer cake”) • A technological roadmap that attempts to realize this vision • The technologies are well-suited to addressing many enterprise software architecture challenges
  • 5. http://www.w3.org/2007/Talks/0130-sb-W3CTechSemWeb/
  • 6. http://www.bnode.org/blog/2009/07/08/the-semantic-web-not-a-piece-of-cake
  • 7. “Focus” standards• Resource Description Framework• Gleaning Resource Descriptions from Dialects of Language• SPARQL Protocol And RDF Query Language• Ontology Web Language
  • 8. RDF• A framework for representing information in on the WWW.• Motivation • machine-interpretable metadata about web resources • mashup of application data • automated processing of web information by software agents• Graph data model (directed, labeled graph)• Nodes and links are labeled with URIs• Some nodes are not labeled (Blank nodes)• Links are called RDF sentences or triples http://www.w3.org/TR/rdf-concepts/
  • 9. GRDDL • A protocol for sowing semantics in structured (XML) web content for harvest • Vast amount of latent semantics in web documents • Web content today is primarily built for human consumptionhttp://www.w3.org/TR/grddl/
  • 10. Faithful Rendition“By specifying a GRDDL transformation, the author of a document states thatthe transformation will provide a faithful rendition in RDF of information (orsome portion of the information) expressed through the XML dialect used inthe source document.”• Licenses an interpretation of an XML document that is certified by the author (embedded) transform XHTML / XML RDF (instances) namespace transform XML namespace RDF
  • 11. Architectural value• XML is well-suited for messaging, data collection, and structural validation• RDF is well-suited for expressive logical assertions, querying, and inference.• RDF graphs can be created, update, deleted, etc. (managed) using a particular XML vocabulary • vocabulary can be specific to a particular purpose• GRDDL facilitates mutually-beneficial use of XML and RDF processing and representation
  • 12. SPARQL • The query language for RDF content • It operates over an RDF dataset • comprised of named (a URI) RDF graphs and a single RDF graph without a name • Operationally and structurally similar to SQL • Many implementations (including the ones we used) build on existing relational database management systems • translate SPARQL queries into SQL queriesElliott et al. A complete translation from SPARQL into efficient SQL. 2009 http://www.w3.org/TR/sparql11-query/
  • 13. OWL• Language for describing and constraining the semantics of an RDF vocabulary• Such constraints (often hierarchical) are called ontologies• An ontology specifies a conceptualization of a particular domain as categories, relationships between them, and constraints on both• By defining an OWL document for the terms in an RDF graph, additional RDF sentences can be inferred• Additionally, an RDF graph can be determined to be consistent or inconsistent with respect to the ontology• Both tasks can be performed by a logical reasoning engine
  • 14. Semantic Database (SDB) • Cleveland Clinic’s Heart and Vascular Institute (HVI) • Challenges: • fragmented gathering and storing of clinical research data • compartmentalization of medical science and practice • clinical knowledge is often expressed in ambiguous, idiosyncratic terminology • problematic for longitudinal patient data that can feasibly span multiple, geographically separated sources and disciplines • Longitudinal patient record: • patient records from different times, providers, and sites of care that are linked to form a lifelong view of a patient’s health care experienceInstitute of Medicine. The computer-based patient record: an essential technology forhealth care. 1997 http://www.w3.org/2001/sw/sweo/public/UseCases/ClevelandClinic/
  • 15. Project goals• Create a framework for context-free data management• Usable for any domain with nothing (or little) assumed about the domain• Expert-provided, domain-specific knowledge is used to control most aspects of • Data entry • Storage • Display • Retrieval • Formatting for external systems
  • 16. Components • Content repository • supports data collection, document management, and knowledge representation for use in managing longitudinal clinical data • manages patient record documents as XML and converts them to RDF graphs for downstream semantic processing • Data collection workflow management • process of transcribing details of a heart procedure from the EHR into a registry • RDF used as the state machine of a workflow enginePierce et al. SemanticDB: A Semantic Web Infrastructure for Clinical Research andQuality Reporting. 2012Ogbuji. A Role for Semantic Web Technologies in Patient Record Data Collection.2009
  • 17. Workflow State as RDF Dataset• Each task is an XML document in a content repository• Mirrored into a named RDF graph that shares a web location (the name) with the document• (SPARQL) query is dispatched against a workflow dataset to find tasks in particular states or assigned to particular people• Applications interact with task information and fetch: • JSON and XML representations (for client-side web applications) • XHTML documents that render as faceted views of a collection of tasks • faceted view includes links to subsequent stages in workflow and into other web applications on server
  • 18. Reporting challenges • Reporting places a heavy burden on institutions to produce data in specific formats with precise definitions • Definitions vary across reports • makes it difficult to use the same source data for all reports • Institutions are typically forced to manually abstract the data for each report • This is done separately to conform to the requirements for each reportPierce et al. SemanticDB: A Semantic Web Infrastructure for Clinical Research andQuality Reporting. 2012
  • 19. Components: reporting • Quality and outcomes reporting • generate outcomes reports both for internal and external consumption • internal reports were generated monthly and external reports are generated quarterly • quarterly reports submitted to Society of Thoracic Surgeons (STS) Adult Cardiac Surgery National Database and American College of Cardiology (ACC) CathPCI Database • submissions are required for certificationPierce et al. SemanticDB: A Semantic Web Infrastructure for Clinical Research andQuality Reporting. 2012
  • 20. Cohort identification • SPARQL and RDF datasets are well-suited as infrastructure for a longitudinal patient record data warehouse • HVI software development team partnered with Cycorp to build a cohort identification interface called the Semantic Research Assistant (SRA) • Based on the Cyc inference engine • a powerful reasoning system and knowledge base with built-in capability for natural language (NL)processing, forward-chaining inference and backward-chaining inference. • incorporates Cycs NL processing to permit a user to compose a cohort selection query by typing an English sentence or sentence fragmentLenat et al. Harnessing Cyc to Answer Clinical Researchers Ad Hoc Queries. 2010.
  • 21. RDF dataset warehouse• CycL to SPARQL • domain-specific medical ontologies in conjunction with the Cyc general ontology are used to convert the NL query into a formal representation and then into SPARQL queries. • SPARQL queries are submitted to the SemanticDB RDF store for execution• Cleveland Clinic’s registry of 200,000 patient records comprises an RDF graph of roughly 80 million RDF assertion
  • 22. Dataset topology• An RDF dataset with no default graph and one named graph per patient record (a patient record graph)• Beyond identifying the cohort, most subsequent query processing happens within a single patient record graph• In our vocabulary, there are instances of PatientRecord, Operation, Patient, MedicalEvent, HospitalEpi sode, etc.• PatientRecord resources share a URI with their containing graph
  • 23. • GRAPH operator can be used to optimize the search space• Optimal for the following cohort querying paradigm • Constraints in the first part of query are cross-graph and the second part are intra-graph
  • 24. Use of system• From 2009 through June of 2011 • over 200 clinical investigations utilized SemanticDB to identify study cohorts and retrieve appropriate data for analysis • studies ranged from relatively simple feasibility assessments to extremely complex investigations of time-related events and competing risks of the patient experiencing a certain outcome after treatment • prior cohort identification and data export queries for studies would have been performed by a skilled database administrator (DBA) interpreting instructions from domain experts • Using SemanticDB and the SRA, a non-technical domain expert performed most of the queries

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