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Semantic Web Technologies for Translational Medicine Vipul Kashyap, PhD [email protected] Senior Medical Informatician, Clinical Knowledge Management and Decision Support Clinical Informatics R&D, Partners Healthcare System Panel 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 Study Reports * Use Case provided by Dr. Tonya Hongsermeier Actionable Decision Support in the 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 and Prognosis Data to Genotypic Data for Cardiomyopathy person concept Z5937X Z5937X Z5937X Z5937X Z5937X Z5956X Z5956X Z5956X Z5956X Z5956X Z5956X Z5956X Z5956X Syncope ER visit Palpitations Gene-Chips Echocardio Gene-Chips Cardiomyop Atrial Fib. Echocardio EKG Cardiac Arr ER Visit Thalamus date 3/4 3/4 3/4 3/4 4/6 5/2 5/2 5/2 5/2 3/9 3/9 3/9 3/9 raw value microarray (encrypted) Gene expression in HCM Test Results Outcomes calculated every week Myectomy Atrial Arrhythymi ER visits Clinic visits Ventricular Arrhy ICD Cong. Heart Failure microarray (encrypted) statistics application server population registry database ownership manager encryption A one slide Introduction to RDF/OWL What is RDF? 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 • 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 • 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 A Strawman Ontology for Translational Medicine OWL ontologies that blend knowledge from the Clinical and Genomic Domains Clinical Knowledge Figure reprinted with permission from Cerebra, Inc. Genomic Knowledge Data Integration Domain Ontologies for Translational Medicine Instantiation Merged RDF Graph RDF Graph 1 RDF Wrapper LIMS Data RDF Graph 2 RDF Wrapper EMR Data 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” “Mr. X” 1 90% degree type name Patient (id = URI1) has_structured_test_result evidence Patient (id = URI1) related_to has_family_history Person (id = URI2) associated_relative MolecularDiagnosticTestResult (id = URI4) identifies_mutation indicates_disease problem FamilyHistory (id = URI3) “Sudden Death” MYH7 missense Ser532Pro (id = URI5) EMR Data 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 Dialated Cardiomyopathy (id = URI6) Bridging Clinical and Genomic Information 90% evidence Dialated Cardiomyopathy (id = URI6) suffers_from “Paternal” “Mr. X” 1 type name degree indicates_disease StructuredTestResult (id = URI4) has_structured_test_result identifies_mutation MYH7 missense Ser532Pro (id = URI5) Patient (id = URI1) related_to has_family_history has_gene Person (id = URI2) associated_relative problem FamilyHistory (id = URI3) RDF Graphs provide a semantics-rich substrate for decision support. Can be exploited by SWRL Rules “Sudden Death” Actionable Decision Support: using SWRL IF the Patient’s structured test result identifies the mutation MYH7 missense:Ser532Pro with confidence ≥ 90% AND the structured test result is indicative of Dialated Cardiomyopathy THEN 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 Propagation IF Molecular Diagnostic reveals MYH7 missense: Ser532Pro or Phe764Leu AND No Structural Heart Disease on Echocardiogram THEN perform DCM monitoring and management protocol Knowledge Update (Hypothetical) IF Molecular Diagnostic reveals MYH7 missense: Ser532Pro AND No Structural Heart Disease on Echocardiogram THEN perform late onset of DCM monitoring protocol If Molecular Diagnostic reveals MYH7 missense Phe764LEU AND No Structural Heart Disease on Echocardiogram THEN 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! 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 Phe764Leu AND No Structural Heart Disease on Echocardiogram THEN perform DCM monitoring and management protocol Knowledge Update (Hypothetical) IF Molecular Diagnostic reveals MYH7 missense: Ser532Pro AND No Structural Heart Disease on Echocardiogram THEN perform late onset of DCM monitoring protocol IF Molecular Diagnostic reveals MYH7 missense Phe764LEU AND No Structural Heart Disease on Echocardiogram THEN perform early onset of DCM monitoring protocol Knowledge Update and Propagation Genotype indicates Rule - genotype_condition - indicates_disease - recommended_intervention Disease indicates recommended_intervention Monitoring Protocol Knowledge Update Genotype2 indicates Genotype1 indicates indicates Monitoring Protocol1 Monitoring Protocol2 Decision Support Use of OWL Inferences for: Logic Update - Keeping knowledge internally consistent - Propagating changes to Dependent Knowledge Artifacts Rule1 - genotype_condition - indicates_disease - recommended_intervention Rule2 - genotype_condition - indicates_disease - recommended_intervention Disease recommended_intervention Update Propagation Updated RDF Graphs are generated from this 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