The presentation discusses the implementation of semantic web technologies in outcomes research, highlighting experiences from the Cleveland Clinic's semantic database project. Key components include RDF, SPARQL, and OWL to facilitate data management, reporting, and cohort identification for patient records. The system has enabled over 200 clinical investigations to utilize semanticdb for efficient cohort selection and data analysis.

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
consumption
http://www.w3.org/TR/grddl/

10. Faithful Rendition
“By specifying a GRDDL transformation, the author of a document states that
the transformation will provide a faithful rendition in RDF of information (or
some portion of the information) expressed through the XML dialect used in
the 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 queries
Elliott 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
experience
Institute of Medicine. The computer-based patient record: an essential technology for
health 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 engine
Pierce et al. SemanticDB: A Semantic Web Infrastructure for Clinical Research and
Quality Reporting. 2012
Ogbuji. 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

19. 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 report
Pierce et al. SemanticDB: A Semantic Web Infrastructure for Clinical Research and
Quality Reporting. 2012

20. 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 certification
Pierce et al. SemanticDB: A Semantic Web Infrastructure for Clinical Research and
Quality Reporting. 2012

22. 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 Cyc's NL processing to permit a user to compose a
cohort selection query by typing an English sentence or sentence
fragment
Lenat et al. Harnessing Cyc to Answer Clinical Researchers' Ad Hoc Queries. 2010.

24. 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

25. 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

26. • 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

27. 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