VAU / BVPDB / ESD / NIP / CDC Public Health Informatics Fellow, PHIFP 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 Good Morning, I am Herman Tolentino, a Public Health Informatics Fellow at the CDC, and I am here to give a talk on computer representation of adverse events following immunizations or AEFI using semantic web technology.

Outline Introduction Methods Results Conclusion I will follow this outline for this presentation. I will give you a brief introduction, followed by a discussion of the methods and project framework, then give you results from the current phase of the project and go to conclusions.

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. First, let’s define what the semantic web is… The semantic web is a mesh of information. It uses a form of XML called Resource Description Framework or RDF to store knowledge. XML makes the knowledge readable by machines. In the semantic web, knowledge is organized as ontologies. Let’s go find out what ontologies are.

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) An ontology is a formal, structured specification of a conceptualization of a body of knowledge. It represents what we know about something, whether it is about wines, cameras, organ systems, epidemics and in our case adverse events. From ontologies, we create knowledge bases to store the formalized specifications. These specifications might include objects and their relationships with one another in a particular domain. We characterize and describe the objects in the domain, for example, “Seizure is an Adverse Event” and we use a language RDF to represent this statement.

Loss of consciousness Seizure History Witnessed Level 2 Manifestations Diagnostic certainty motor manifestations To give you a mental picture of what an ontology of adverse events is, we can imagine a conceptual graph containing something like this using Seizure as an example of an adverse event. In this picture, we have seizure. And we state that seizure has the following clinical manifestations: loss of consciousness and motor manifestations. [CLICK] The presence of witnessed loss of consciousness and motor manifestations indicate a Level 1 of Diagnostic Certainty for our case definition, while a history of loss of consciousness with motor manifestations indicate a Level 2 of Diagnostic Certainty. Tonic Clonic Tonic-clonic

Uses of Ontologies in Public Health Semantic interoperability Semantic enhancement Knowledge repository Let’s look at the other uses of ontologies in public health. Let me introduce three informatics terms: Semantic interoperability Semantic enhancement And as a knowledge repository We will go into a bit of detail for each one in the following slides.

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 Let us talk about semantic interoperability. In public health, we want our systems to be able to talk to each other and be able to exchange data that have consistent meaning or semantics. This is what we mean by semantic interoperability. Semantic problems arise because of words spelled the same way but mean different things, like fundus. This word, fundus, carries different meanings: to an ophthalmologist when he talks about a part of the eye to an obstetrician when he talks about a part of the uterus and to a gastroenterologist when he talks about a part of the stomach. Non-medical examples would be: Fall, referring to a season, and Fall, for falling down.

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 Semantic enhancement, on the other hand, applies to situations when we want to get to all possible representations of a disease, given a particular code in a public health report or a record entry in a surveillance system. We do this by searching for related concepts in the Unified Medical Language System (UMLS). The UMLS is a set of controlled vocabularies organized by concepts. For example, if we encounter the code for hepatitis, whether it is ICD or SNOMED, we would also want to find out if there are related codes in the surveillance system that represent jaundice, icterus or hepatomegaly, which are concepts related to hepatitis. This way we cast a bigger net to capture the universe of hepatitis.

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 Lastly, ontologies serve as knowledge repositories. We need knowledge repositories to store knowledge. The side effect of rapid advances in health care and related technologies is that we drown in so much knowledge and information, we end up with a situation called knowledge explosion. Dr. Koop, a former surgeon general once said that even if a conscientious physician read 2 journals a day he would have lagged behind by 800 years worth of knowledge after one year. Fortunately for us, rapid advances in and the decreasing cost of technology allow us to store this rapidly expanding body of knowledge in very large storage devices to be manipulated by powerful computers. Think of what Medline or Pubmed has done for us.

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) We have named our project OVAE for Ontology for Vaccine Adverse Events. With this project we aim to Digitally represent standardized case definitions so that these case definitions can be read from the Internet by computers Enhance detection of cases in surveillance systems by connecting them to this ontology of adverse event case definitions. Provide stakeholders with a way of submitting questions to and getting the answers from the stored knowledge in the adverse event ontology

Framework RDF Case Definitions UMLS Adverse Events Following Immunization Ontology User Interface Creation and Inferencing Phase I Phase II Phase III Conceptual Model This diagram shows the framework for the project. The project is divided into three phases, we are currently in phase 1. After conceptualization in Phase 1, we take case definitions developed by the Brighton Collaboration and convert them to RDF. Down here [point to UMLS], we take the UMLS concepts and convert them to RDF. Next, in ontology integration, we take both RDF files and merge them in phase 2. Then we create the web interface so that stakeholders can ask questions of the ontology in Phase 3.

Results Conceptual model of adverse events (BC) Translation of case definitions and UMLS concepts to Resource Description Framework (RDF) format Let me talk about these two things

1 2 3 4 5 ADVERSE EVENTS CASE DEFINITIONS CONTROLLED VOCABULARIES Here is the conceptual model for adverse events that we created using a modeling tool. Just pay attention to the red arrows as these components or objects are our main and early focus for creating the ontology. For ontology creation, we are focusing on adverse events, their case definitions, the clinical manifestations associated with the case definitions, how they relate to controlled vocabularies and how combinations of clinical manifestations enable us to determine the level of diagnostic certainty of the case definition. CLINICAL MANIFESTATIONS 4 LEVEL OF DIAGNOSTIC CERTAINTY 5

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 Going back to the Seizure example… BC case definitions provide different levels of diagnostic certainty depending on clinical manifestations reported. Boolean expressions, knowledge constraints and structured descriptions help us in translating natural language logic to RDF logic.

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. We use the Unified Medical Language System (UMLS) of the National Library of Medicine in the following manner: UMLS concepts are identified using Concept Unique Identifiers (CUIs). We embed these in the adverse event ontology. Controlled vocabulary codes are related to concepts. We find these codes in adverse event reports and surveillance systems. Using the UMLS and the adverse event ontology, we can semantically enhance event detection by getting to related concepts indicated by controlled vocabulary codes.

RDF file of a UMLS table Here is an RDF file generated out of a UMLS table.

Concept Unique Identifier (CUI) Loss of consciousness Seizure Manifestations Tonic Clonic Tonic-clonic Level 1 History Witnessed Level 2 Diagnostic certainty C000001 C000002 UMLS concepts codes ICD9 Concept Unique Identifier (CUI) Motor manifestations [SEIZURE CASE DEFINITION] This is the same picture I showed you earlier. [AFTER CLICK UMLS FADES IN] And this is how the UMLS concepts fit into the adverse event ontology picture. The UMLS contains 100+ coding systems and concepts. The coding system used could also be ICD9CM, SNOMED and other adverse event coding terminologies such as COSTART, MedDRA or WHO-ART. [POINT TO C000002] This is a UMLS Concept Unique Identifier.

Conclusions The domain of ontology creation is an emerging field in health care. Advances in technology permit creation of large- scale ontologies. We can use ontologies for semantic interoperability and semantic enhancement and as knowledge repositories. Collaboration leverages partners’ resources and fosters shared learning. I conclude for now by saying that … The domain of ontology creation is an emerging field in health care. Its importance in public health as it encompasses multiple domains cannot be understated. The advances in technology permit creation of large scale ontologies that are available online and can be queried with existing tools. I have introduced to you how we can use ontologies in public health: for semantic interoperability and semantic enhancement and for use as a knowledge repository. Collaboration in ontology work leverages the partners’ resources and fosters shared learning in this exciting field.

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. In the future, we hope to be able to merge the two ontologies, and create the web-based interface for stakeholders.

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 Acknowledgements should go to our partners in the Brighton Collaboration and the National Library of Medicine Office of High Performance Communications and Computing.

Correspondence Herman Tolentino, MD HTolentino@CDC.gov Daniel Payne, MSPH, PhD DPayne@CDC.gov BVPDB / ESD / NIP / CDC 1600 Clifton Road NE, Mailstop E-61 Atlanta, GA 30329 Thank you and you may address any correspondence to me or my mentor.