Semantic Web Technologies for Translational Medicine

Vipul Kashyap, PhDvkashyap1@partners.org

Senior Medical Informatician, Clinical Knowledge Management and Decision Support

Clinical Informatics R&D, Partners Healthcare SystemPanel on “Towards a Semantic Web for the Life Sciences?”

October 24, 2005

Outline• Translational Medicine Use Case

— Translation of Genomic Research Insights into Clinical Care

• Key Functionalities— Data Integration— Actionable Decision Support— Knowledge Update and Propagation

• Semantic Web Technologies— RDF: Resource Description Framework— OWL: Web Ontology Language— SWRL: Semantic Web Rules Language

• Conclusions

Translational Medicine Use Case*:Dr. Genomus Meets Basketball Player Who fainted at Practice

• Clinical exam reveals abnormal heart sounds

• Family History: Father with sudden death at 40,

• 2 younger brothers apparently normal

• Ultrasound ordered based on clinical exam reveals cardiomyopathy

Structured Physical Exam

Structured Family History

Structured Imaging StudyReports

* Use Case provided by Dr. Tonya Hongsermeier

Actionable Decision Support inthe Workflow Context

Echo triggers guidance to screen for possible mutations:- MYH7, MYBPC3, TNN2, TNNI3, TPM1, ACTC, MYL2, MYL3

Knowledge-based Decision Support

Connecting Dx, Rx, Outcomes andPrognosis Data to Genotypic Data for Cardiomyopathy

statisticsapplication

server

statisticsapplication

server

Gene expression in HCM Test Results

MyectomyAtrial Arrhythymi

ER visitsClinic visits

Outcomes calculated every weekSyncopeER visit

microarray (encrypted)

ownershipmanager

encryption

Palpitations

Gene-Chips

populationregistry

databasedatabase

microarray (encrypted)

Ventricular ArrhyICD

Cong. Heart Failure

ER Visit

EKGCardiac Arr

Thalamus

person concept date

Gene-ChipsEchocardio

CardiomyopAtrial Fib.Echocardio

Z5937XZ5937XZ5937XZ5937X

Z5956XZ5956XZ5956XZ5956X

Z5956XZ5956XZ5956XZ5956X

Z5937X

raw value

3/43/43/43/4

3/93/93/93/9

5/25/25/25/2

4/6

A one slide Introduction to RDF/OWLWhat is RDF?

• Resource Description Framework – description of any resource

• Triples <resource, property, value>, e.g., <URI1, “name”, “Mr. X”>

— Nodes: “URI1”, “Mr. X”— Edge: “name”

• Graph based Data Model

• RDF graphs are instances of ontological elements

What is OWL?

• Web Ontology Language – description of knowledge and ontologies of a given domain

• Axioms/constraints capture knowledge about a given domain, e.g.,

— class(Patient), class(Person)— Patient Person

• Lattice Organization

• Axioms/constraints are imposed on underlying RDF Graph instances

• URIs (URLs) are used as identifiers for:• Resources, Properties, Values, Namespaces and Ontological Elements

• Namespaces contain:• Tags for RDF and OWL languages• Ontological elements (classes, properties) that are instantiated by these RDF Graphs• Ontological elements or XML Schema datatypes that are dimensions of identifiers such as LSIDs

Clinical Knowledge

Genomic KnowledgeFigure reprinted withpermission from Cerebra, Inc.

A Strawman Ontology for Translational Medicine

OWL ontologies that blend knowledgefrom the Clinical and Genomic Domains

Data IntegrationDomain Ontologiesfor Translational Medicine

LIMS Data EMR Data

RDF Wrapper RDF Wrapper

RDF Graph 1 RDF Graph 2

Merged RDF Graph

Instantiation

Use of RDF graphs that instantiate these ontologies:-- Rules/semantics-based integration independent of location, method of access or underlying data structures!- Highly configurable, minimize

software coding

Bridging Clinical and Genomic Information“Paternal” 1

type degree

Patient(id = URI1)

“Mr. X”

name

Person(id = URI2)

related_to

FamilyHistory(id = URI3)

has_family_history

“Sudden Death”problem

associated_relative

EMR Data

Patient(id = URI1)

MolecularDiagnosticTestResult(id = URI4)

has_structured_test_result

MYH7 missense Ser532Pro(id = URI5)

identifies_mutation

DialatedCardiomyopathy(id = URI6)

indicates_disease

LIMS Data

Rule/Semantics-based Integration:- Match Nodes with same Ids- Create new links: IF a patient’s structured test result indicates a disease THEN add a “suffers from link” to that disease

90%

evidence

Bridging Clinical and Genomic Information

Patient(id = URI1)

“Mr. X”

name

Person(id = URI2)

related_to

FamilyHistory(id = URI3)

has_family_history

“Sudden Death”problem

“Paternal” 1

type degree

associated_relative

StructuredTestResult(id = URI4)

MYH7 missense Ser532Pro(id = URI5)

identifies_mutation

DialatedCardiomyopathy(id = URI6)

indicates_disease

has_structured_test_result

suffers_from

has_gene

RDF Graphs provide a semantics-rich substrate for decision support. Can be exploited by SWRL Rules

90%

evidence

Actionable Decision Support:using SWRLIF the Patient’s structured test result identifies the mutation MYH7

missense:Ser532Pro with confidence ≥ 90%AND the structured test result is indicative of Dialated CardiomyopathyTHEN Patient suffers from Dialated CardioMyopathy Patient has gene MYH7missense:Ser532Pro Perform DCM monitoring and management protocol on the Patient.

patient(?p) & molecular_diagnostic_test(?t) & has_structured_test_result(?p, ?t) & identifies_mutation(?t, “MYH7 missense:Ser532Pro”) & indicates_disease(?t, “Dialated Cardiomyopathy”) suffers_from(?p, “Dialated Cardiomyopathy”)

has_gene(?p, “MYH7 missense:Ser532Pro)recommended_intervention(“DCM Monitoring and Management”)

Semantic Web Rules Language (SWRL)

• References to ontological concepts and relationships— Describe clinical and genomic information

• Can be used to infer patient state:— Patient has a particular gene/mutation— Patient suffers from a particular disease

• Can be used to recommend clinical care:— Order Monitoring and Management Protocol

patient(?p) & molecular_diagnostic_test(?t) & mutation(?m) & disease(?d)has_structured_test_result(?p, ?t) & identifies_mutation(?t, ?m) & indicates_disease(?t, ?d) & suggested_protocol(?d, ?pro) suffers_from(?p, ?d)

has_gene(?p, ?m)order_protocol(?pro)

Knowledge Update and PropagationIF Molecular Diagnostic reveals MYH7 missense: Ser532Pro or Phe764LeuAND No Structural Heart Disease on EchocardiogramTHEN perform DCM monitoring and management protocol

IF Molecular Diagnostic reveals MYH7 missense: Ser532ProAND No Structural Heart Disease on EchocardiogramTHEN perform late onset of DCM monitoring protocol

If Molecular Diagnostic reveals MYH7 missense Phe764LEUAND No Structural Heart Disease on EchocardiogramTHEN perform early onset of DCM monitoring protocol

• Discovery of New Genotypes• Invention of New Monitoring Protocols• Discovery of Associations between Genotype, Disease and Monitoring Protocols• Modification of Decision Support Rules to Reflect This Modifies resultant RDF graphs generated!

KnowledgeUpdate(Hypothetical)

Knowledge Update and Propagation

• Discovery of New Genotypes• Invention of New Monitoring Protocols• Discovery of Associations between Genotype, Disease and Monitoring Protocols• Modification of Decision Support Rules to Reflect This Modifies resultant RDF graphs generated!

IF Molecular Diagnostic reveals MYH7 missense: Ser532Pro or Phe764LeuAND No Structural Heart Disease on EchocardiogramTHEN perform DCM monitoring and management protocol

IF Molecular Diagnostic reveals MYH7 missense: Ser532ProAND No Structural Heart Disease on EchocardiogramTHEN perform late onset of DCM monitoring protocol

IF Molecular Diagnostic reveals MYH7 missense Phe764LEUAND No Structural Heart Disease on EchocardiogramTHEN perform early onset of DCM monitoring protocol

KnowledgeUpdate(Hypothetical)

Knowledge Update and PropagationGenotype Disease

indicates

MonitoringProtocol

indicatesrecommended_intervention

Rule- genotype_condition- indicates_disease- recommended_intervention

Genotype1 Disease

MonitoringProtocol1

indicates recommended_intervention

Genotype2

indicates

MonitoringProtocol2

indicates

Rule1- genotype_condition- indicates_disease- recommended_interventionRule2- genotype_condition- indicates_disease- recommended_intervention

KnowledgeUpdate

UpdatePropagation

Decision SupportLogic Update

Use of OWL Inferences for:- Keeping knowledge internally consistent- Propagating changes to Dependent Knowledge Artifacts

Updated RDF Graphsare generated fromthis point on!

Conclusions

• Translational Medicine is a knowledge intensive field. The ability to capture semantics of this knowledge is crucial for implementation.

• Personalized Medicine cannot be implemented in an scalable, efficient and extensible manner without Semantic Web technologies

• The rate of Knowledge Updates will change drastically as Genomic knowledge explodes

• Automated Semantics-based Knowledge Update and Propagation will be key in keeping the knowledge updated and current