Semantic Web Technologies: A Paradigm for Medical Informatics

1. Semantic Web Technologies: A Paradigm for Medical Informatics Chimezie Ogbuji (Owner, Metacognition LLC.) http://metacognition.info/presentations/SWTMedicalInformatics.pdf http://metacognition.info/presentations/SWTMedicalInformatics.ppt

2. Who I am • Circa 2001: Introduced to web standards and Semantic Web technologies • 2003-2011: Lead architect of CCF in-house clinical repository project • 2006-2011: Member representative of CCF in World-wide Web Consortium (W3C) • Editor of various standards and Semantic Web Health Care and Life Sciences Interest Group chair • 2011-2012: Senior Research Associate at CWRU Center for Clinical Investigations • 2012-current: Started business providing resource and data management software for home healthcare agencies (Metacognition LLC)

3. Medical Informatics Challenges • Semantic interoperability • Exchange of data with common meaning between sender and receiver • Most of the intended benefits of HIT depend on interoperability between systems • Difficulties integrating patient record systems with other information resources are among the major issues hampering their effectiveness • Interoperability is a major goal for meaningful use of Electronic Health Records (EHR) Rodrigues et al. 2013; Kadry et al. 2010; Shortliffe and Cimino, 2006

4. Requirements and Solutions • Semantic interoperability requires: • Structured data • A common controlled vocabulary • Solutions emphasize the meaning of data rather than how they are structured • “Semantic” paradigms

5. Registries and Research DBs • Patient registries and clinical research repositories capture data elements in a uniform manner • The structure of the underlying data needs to be able to evolve along with the investigations they support • Thus, schema extensibility is important

6. Querying Interfaces • Standardized interfaces for querying facilitate: • Accessibility to clinical information systems • Distributed querying of data from where they reside • Requires: • Semantically-equivalent data structures • Alternatively, data are centralized in data warehouses Austin et al. 2007, “Implementation of a query interface for a generic record server”

7. Biomedical Ontologies • Ontologies are artifacts that conceptualize a domain as a taxonomy of classes and constraints on relationships between their members • Represented in a particular formalism • Increasingly adopted as a foundation for the next generation of biomedical vocabularies • Construction involves representing a domain of interest independent of behavior of applications using an ontology • Important means towards achieving semantic interoperability

8. Biomedical Ontology Communities • Prominent examples of adoption by life science and healthcare terminology communities: • The Open Biological and Biomedical Ontologies (OBO) Foundry • Gene Ontology (GO) • National Center for Biomedical Ontology (NCBO) Bioportal • International Health Terminology Standards Development Organization (IHTSDO)

9. Semantic Web and Technologies • The Semantic Web is a vision of how the existing infrastructure of the World-wide Web (WWW) can be extended such that machines can interpret the meaning of data on it • Semantic Web technologies are the standards and technologies that have been developed to achieve the vision

10. An Analogy • (Technological) singularity is a theoretical moment when artificial intelligence (AI) will have progressed to a greater-than-human intelligence • Despite remaining in the realm of science fiction, it has motivated many useful developments along the way • The use of ontologies for knowledge representation and IBM Watson capabilities, for example

11. Background: Graphs • Graphs are data structures comprising nodes and edges that connect them • The edges can be directional • Either the nodes, the edges, or both can be labeled • The labels provide meaning to the graphs (edge labels in particular)

12. Resource Description Framework • The Resource Description Framework (RDF) is a graph-based knowledge representation language for describing resources • It’s edges are directional and both nodes and edges are labeled • It uses Universal Resource Identifiers (URI) for labeling • Foundation for Semantic Web technologies

13. RDF: Continued • The edges are statements (triples) that go from a subject to an object • Some objects are text values • Some subjects and objects can be left unlabeled (Blank nodes) • Anonymous resources: not important to label them uniquely • The URI of the edge is the predicate • Predicates used together for a common purpose are a vocabulary

14. Subject: Dr. X (a URI) • Object: Chime • Predicate: treats • Vocabulary: • treats, subject of record, author, and full name

15. RDF vocabularies • How meaning is interpreted from an RDF graph • There are vocabularies that constrain how predicates are used • Want a sense of treats where the subject is a clinician and the object is a patient • There is a predicate relating resources to the classes they are a member of (type) • There are vocabularies that define constraints on class hierarchies • These comprise a basic RDF Schema (RDFS) language • Represented as an RDF graph

17. Ontologies for RDF • The Ontology Web Language (OWL) is used to describe ontologies for RDF graphs • More sophisticated constraints than RDFS • Commonly expressed as an RDF graph • Defines the meaning of RDF statements through constraints: • On their predicates • On the classes the resources they relate belong to

19. OWL Formats • Most common format for describing ontologies • Distribution format of ontologies in the NCBO BioPortal • SNOMED CT distributions include an OWL representation • RDF graphs can describe medical content in a SNOMED CT-compliant way through the use of this vocabulary

20. Validation and Deduction • OWL is based on a formal, mathematical logic that can be used for validating the structure of an ontology and RDF data that conform to it (consistency checking) • Used to deduce additional RDF statements implied by the meaning of a given RDF graph (logical inference) • Logical reasoners are used for this

21. Inference • Can infer anatomical location from SNOMED CT definitions Hypertension DX <-> 1201005 / “Benign essential hypertension (disorder)”

22. Querying RDF Graphs • SPARQL is the official query language for RDF graphs • Comparable to relational query languages • Primary difference: it queries RDF triples, whereas SQL queries tables of arbitrary dimensions • Includes various web protocols for querying RDF graphs • Foundation of SPARQL is the triple pattern • (?clinician, treats, ?patient) • ?clinician and ?patient are variables (like a wildcard)

23. Which physicians have given essential hypertension diagnoses and to whom? (?physician, author, ?dx) (?physician, treats, ?patient) (?dx, subject of record, ?patient) (?dx, type, Hypertension DX)

24. SPARQL over Relational Data • Most common implementations convert SPARQL to SQL and evaluate over: • a relational databases designed for RDF storage • an existing relational database • There are products for both approaches • Former requires native storage of RDF • Relational structure doesn’t change even as RDF vocabulary does (schema extensibility) Elliot et al. 2009, “A Complete Translation from SPARQL into Efficient SQL”

25. SPARQL over Existing Relation Data • “Virtual RDF view” • Translation to SQL follows a given mapping from existing relational structures to an RDF vocabulary • Allows non-disruptive evolution of existing systems • Well-suited as a standard querying interface over clinical data repositories • They can be queried as SPARQL, securely over encrypted HTTP

27. Example: Cleveland Clinic (SemanticDB) • Content repository and data production system released in Jan. 2008 • 80 million (native) RDF statements • Uses vocabulary from a patient record OWL ontology for the registry • Based on • Existing registry of heart surgery and CV interventions • 200,000 patient records • Generating over 100 publications per year Pierce et al. 2012, “SemanticDB: A Semantic Web Infrastructure for Clinical Research and Quality Reporting”

28. Cohort Identification • Interface developed in conjunction with Cycorp • Leverage their logical reasoning system (Cyc) • Identifies cohorts using natural language (NL) sentence fragments • Converts fragments to SPARQL • SPARQL is evaluated against RDF store

29. Example: Mayo Clinic (MCLSS) • Mayo Clinic Life Sciences System (MCLSS) • Effort to represent Mayo Clinic EHR data as RDF graphs • Patient demographics, diagnoses, procedures, lab results, and free-text notes • Goal was to wrap MCLSS relational database and expose as read-only, query-able RDF graphs that conform to standard ontologies • Virtual RDF view Pathak et al. 2012, "Using Semantic Web Technologies for Cohort Identification from Electronic Health Records for Clinical Research"

30. Example: Mayo Clinic (CEM) • Clinical Element Model (CEM) • Represents logical structure of data in EHR • Goal: translate CEM definitions into OWL and patient (instance) data into conformant RDF • Use tools (logical reasoners) to check semantic consistency of the ontology, instance data, and to extract new knowledge via deduction • Instance data validation: • correct number of linked components, value within data range, existence of units, etc. Tao et al. 2012, ”A semantic-web oriented representation of the clinical element model for secondary use of electronic health records data"

31. Summary • Schema extensibility • Use of RDF • Semantic Interoperability • Domain modeling using OWL and RDFS • Standardized query interfaces • Querying over SPARQL • Incremental, non-disruptive adoption • Virtual RDF views • Main challenge: highly disruptive innovation