Semantic Web Technologies: A Paradigm for Medical InformaticsChimezie Ogbuji (Owner, Metacognition LLC.)

http://metacognition.info/presentations/SWTMedicalInformatics.pdfhttp://metacognition.info/presentations/SWTMedicalInformatics.ppt

Who I amCirca 2001: Introduced to web standards and

Semantic Web technologies2003-2011: Lead architect of CCF in-house

clinical repository project2006-2011: Member representative of CCF in

World-wide Web Consortium (W3C)◦ Editor of various standards and Semantic Web Health

Care and Life Sciences Interest Group chair2011-2012: Senior Research Associate at

CWRU Center for Clinical Investigations2012-current: Started business providing

resource and data management software for home healthcare agencies (Metacognition LLC)

Medical Informatics ChallengesSemantic 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

Requirements and SolutionsSemantic interoperability

requires:◦Structured data◦A common controlled vocabulary

Solutions emphasize the meaning of data rather than how they are structured◦“Semantic” paradigms

Registries and Research DBsPatient 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

Querying InterfacesStandardized interfaces for

querying facilitate:◦Accessibility to clinical information

systems◦Distributed querying of data from

where they resideRequires:

◦Semantically-equivalent data structuresAlternatively, data are centralized

in data warehouses

Austin et al. 2007, “Implementation of a query interface for a generic record server”

Biomedical OntologiesOntologies are artifacts that conceptualize a

domain as a taxonomy of classes and constraints on relationships between their members

Represented in a particular formalismIncreasingly adopted as a foundation for the

next generation of biomedical vocabulariesConstruction involves representing a domain

of interest independent of behavior of applications using an ontology

Important means towards achieving semantic interoperability

Biomedical Ontology CommunitiesProminent 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)

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

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

Background: GraphsGraphs are data structures

comprising nodes and edges that connect them

The edges can be directionalEither the nodes, the edges, or

both can be labeledThe labels provide meaning to

the graphs (edge labels in particular)

Resource Description FrameworkThe 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

RDF: ContinuedThe edges are statements (triples)

that go from a subject to an objectSome objects are text valuesSome subjects and objects can be

left unlabeled (Blank nodes)◦Anonymous resources: not important to

label them uniquelyThe URI of the edge is the predicatePredicates used together for a

common purpose are a vocabulary

Subject: Dr. X (a URI)Object: ChimePredicate: treatsVocabulary:

◦treats, subject of record, author, and full name

RDF vocabulariesHow meaning is interpreted from an RDF graphThere 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 hierarchiesThese comprise a basic RDF Schema (RDFS)

languageRepresented as an RDF graph

Ontologies for RDFThe Ontology Web Language (OWL) is

used to describe ontologies for RDF graphs

More sophisticated constraints than RDFS

Commonly expressed as an RDF graphDefines the meaning of RDF statements

through constraints:◦On their predicates◦On the classes the resources they relate

belong to

OWL FormatsMost common format for

describing ontologiesDistribution format of ontologies

in the NCBO BioPortalSNOMED CT distributions include

an OWL representation◦RDF graphs can describe medical

content in a SNOMED CT-compliant way through the use of this vocabulary

Validation and DeductionOWL 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

InferenceCan infer anatomical location

from SNOMED CT definitions

Hypertension DX <-> 1201005 / “Benign essential hypertension (disorder)”

Querying RDF GraphsSPARQL is the official query language for

RDF graphsComparable 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)

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)

?physician ?patient ?dx

Dr. X Chime …

SPARQL over Relational DataMost 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 approachesFormer 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”

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

Example: Cleveland Clinic (SemanticDB)Content repository and data production

system released in Jan. 200880 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”

Cohort IdentificationInterface developed in

conjunction with CycorpLeverage their logical reasoning

system (Cyc)◦Identifies cohorts using natural

language (NL) sentence fragments◦Converts fragments to SPARQL◦SPARQL is evaluated against RDF

store

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 viewPathak et al. 2012, "Using Semantic Web Technologies for Cohort Identification from Electronic Health Records for Clinical Research"

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"

SummarySchema extensibility

◦Use of RDFSemantic Interoperability

◦Domain modeling using OWL and RDFSStandardized query interfaces

◦Querying over SPARQLIncremental, non-disruptive adoption

◦Virtual RDF viewsMain challenge: highly disruptive

innovation