Computer Representation of Adverse Events Following Immunizations Using

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Computer Representation of Adverse Events Following Immunizations Using Semantic Web Technology Herman Tolentino, MD

Computer Representation of Adverse Events Following Immunizations Using Semantic Web Technology Herman Tolentino, MD VAU / BVPDB / ESD / NIP / CDC Public Health Informatics Fellow, PHIFP March 24, 2004

Outline o Introduction o Methods o Results o Conclusion

Outline o Introduction o Methods o Results o Conclusion

Semantic Web • A mesh of information • Uses a form of XML called

Semantic Web • A mesh of information • Uses a form of XML called Resource Description Framework (RDF) to store knowledge • Readable by machines • Knowledge is organized as ontologies.

What is an ontology? • A formal, structured conceptualization of a body of knowledge

What is an ontology? • A formal, structured conceptualization of a body of knowledge = what we know about something (e. g. , adverse events) • Knowledge base = how we represent the ontology in a system • Our body of knowledge is adverse events following immunization (AEFI)

Seizure Loss of consciousness History Witnessed Manifestations motor manifestations Tonic Clonic Tonic-clonic Level 1

Seizure Loss of consciousness History Witnessed Manifestations motor manifestations Tonic Clonic Tonic-clonic Level 1 Level 2 Diagnostic certainty

Uses of Ontologies in Public Health • Semantic interoperability • Semantic enhancement • Knowledge

Uses of Ontologies in Public Health • Semantic interoperability • Semantic enhancement • Knowledge repository

Uses of Ontologies in Public Health Semantic Interoperability • Exchange of data at the

Uses of Ontologies in Public Health Semantic Interoperability • Exchange of data at the level of meaning • Semantic problem example: fundus, pyogenic granuloma • Information systems understand each other and allow for flexible retrieval of information

Uses of Ontologies in Public Health Semantic Enhancement • Getting to all possible representations

Uses of Ontologies in Public Health Semantic Enhancement • Getting to all possible representations of a controlled vocabulary code • By searching for related concepts in the Unified Medical Language System (UMLS) • Hepatitis - related concepts would be jaundice, icterus, hepatomegaly

Uses of Ontologies in Public Health Knowledge Repository • Knowledge explosion: If a physician

Uses of Ontologies in Public Health Knowledge Repository • Knowledge explosion: If a physician reads only 2 journals a day he will be behind by 800 years worth of knowledge at the end of one year (Koop, 1999). • Enabled by rapid advances in and decreasing costs of technology

Adverse Event Ontology Project • OVAE (Ontology for Vaccine Adverse Events) • Problems addressed:

Adverse Event Ontology Project • OVAE (Ontology for Vaccine Adverse Events) • Problems addressed: • Digitally represent standardized case definitions (semantic interoperability) • Cast a wider net for cases in surveillance systems by getting to related codes using the ontology and UMLS (semantic enhancement) • Knowledge management (repository)

Framework Conceptual Model Case Definitions RDF UMLS RDF Phase I Adverse Events Following Immunization

Framework Conceptual Model Case Definitions RDF UMLS RDF Phase I Adverse Events Following Immunization Ontology Phase II User Interface Creation and Inferencing Phase III

Results o Conceptual model of adverse events (BC) o Translation of case definitions and

Results o Conceptual model of adverse events (BC) o Translation of case definitions and UMLS concepts to Resource Description Framework (RDF) format

1 ADVERSE EVENTS 2 CASE DEFINITIONS 3 CONTROLLED VOCABULARIES 4 CLINICAL MANIFESTATIONS 5 LEVEL

1 ADVERSE EVENTS 2 CASE DEFINITIONS 3 CONTROLLED VOCABULARIES 4 CLINICAL MANIFESTATIONS 5 LEVEL OF DIAGNOSTIC CERTAINTY

Seizure Case Definition • Level 1 Diagnostic Certainty • Witnessed sudden loss of consciousness

Seizure Case Definition • Level 1 Diagnostic Certainty • Witnessed sudden loss of consciousness AND • Generalized, tonic, clonic, tonic-clonic, OR atonic motor manifestations • Level 2 Diagnostic Certainty • History of unconsciousness AND • Generalized, tonic, clonic, tonic-clonic, OR atonic motor manifestations

How the UMLS is Used • UMLS concepts are identified using Concept Unique Identifiers

How the UMLS is Used • UMLS concepts are identified using Concept Unique Identifiers (CUIs). • Controlled vocabulary codes are related to concepts. • Using the UMLS and the adverse event ontology, we can semantically enhance event detection by linking concepts in the ontology to concepts in the UMLS.

RDF file of a UMLS table

RDF file of a UMLS table

Seizure Loss of consciousness Concept Unique Identifier (CUI) Manifestations C 000001 Witnessed ICD 9

Seizure Loss of consciousness Concept Unique Identifier (CUI) Manifestations C 000001 Witnessed ICD 9 concepts C 000002 Motor manifestations Tonic Clonic History UMLS Level 1 codes Level 2 ICD 9 Tonic-clonic Diagnostic certainty

Conclusions • The domain of ontology creation is an emerging field in health care.

Conclusions • The domain of ontology creation is an emerging field in health care. • Advances in technology permit creation of largescale ontologies. • We can use ontologies for semantic interoperability and semantic enhancement and as knowledge repositories. • Collaboration leverages partners’ resources and fosters shared learning.

Future Directions • Phase 1: Continue ontology development • Phase 2: Merge two conceptual

Future Directions • Phase 1: Continue ontology development • Phase 2: Merge two conceptual domains: vaccine adverse events and UMLS. • Phase 3: Create query interface for inferencing engine.

Acknowledgements • Brighton Collaboration - a voluntary organization of international experts that develops standardized

Acknowledgements • Brighton Collaboration - a voluntary organization of international experts that develops standardized case definitions for AEFI • Office of High Performance Computing and Communications of the National Library of Medicine

Correspondence Herman Tolentino, MD HTolentino@CDC. gov Daniel Payne, MSPH, Ph. D DPayne@CDC. gov BVPDB

Correspondence Herman Tolentino, MD HTolentino@CDC. gov Daniel Payne, MSPH, Ph. D DPayne@CDC. gov BVPDB / ESD / NIP / CDC 1600 Clifton Road NE, Mailstop E-61 Atlanta, GA 30329

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