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1 Project Scope S emantic interoperability C ommunity o f P ractice E nablement for: –Federal Health Architecture (FHA) –National Health Information Network.

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Presentation on theme: "1 Project Scope S emantic interoperability C ommunity o f P ractice E nablement for: –Federal Health Architecture (FHA) –National Health Information Network."— Presentation transcript:

1 1 Project Scope S emantic interoperability C ommunity o f P ractice E nablement for: –Federal Health Architecture (FHA) –National Health Information Network (NHIN) DRM Semantic Technologies Profile Pilot by –Ontolog Forum, –Stanford Medical Informatics, –SICoP

2 2 Outline Participants Purpose Topics Demonstrations –Protégé based EON –Time Representations Next Steps

3 3 Participants ONTOLOG Forum : –an open, international, citizen-centric, virtual community of practice working on business domain ontologies. –http://ontolog.cim3.net Stanford Medical Informatics (SMI): –an interdisciplinary academic and research group within the Department of Medicine in the Stanford University School of Medicine –brings together scientists who create and validate models of how knowledge and data are used within biomedicine. – Semantic Interoperability Community of Practice (SICoP): –chartered by the CIO Councils Best Practices Committee for the purpose of achieving "semantic interoperability" and "semantic data integration" –focused on the government sector and to make the Semantic Web operational. –http://web-services.gov

4 4 Purpose to see how ontologies can help architects define their architecture to understand how ontologies will help architects drive interoperability in ways that: –avoid remedial work down the road –avoid false starts

5 5 Specific Objectives Provide Semantic Technology Profiles (STP) for the Data Reference Model (DRM). –See bin/ Support the Federal Health Architectures (FHA) Architecture Development Methodology (ADM) and the Architecture Peer Review Group (APRG). –See (password required) Support the National Health Information (NHIN) Network Request for Information (RFI) Review. –See

6 6 Main Topics (by Slide numbers) Ontologies for Semantic Interoperability in Enterprise Architecture (Slide # 8) Formal Taxonomies as Ontologies (Slide # 9) Ontology-Driven Information Systems (Slide #10) Categories of Ontologies (Slides # 11-12) FEA Reference Model Ontology (FEA-RMO) (Slide #13) Business Reference Model (BRM) Taxonomy (Slide #14) Health Domains Taxonomy (Slides #15-16) The Role of Ontologies in the Electronic Health Record (Slide #17)

7 7 Ontologies for Semantic Interoperability in Enterprise Architecture Age of Programs Age of Proprietary Data Age of Open Data Age of Open Metadata Age of Semantic Models Program-Data GIGO/minis/microswww / NetscapeWeb servicesOWL Text, Office Docs Databases (proprietary schema) HTML, XML (open schema) Namespaces, Taxonomies, RDF Ontologies & Inference Procedural Programming Object-Oriented Programming Model-Driven Programming less Data is less important than code as Data is as important as code more Data is more important than code Michael Daconta, Creating Relevance and Reuse with Targeted Semantics, XML 2004 Conference Keynote, November 16, 2004.

8 8 Formal Taxonomies as Ontologies OWL Listing: Etc. Formal Taxonomies for the U.S. Government, Michael Daconta, Metadata Program Manager, US Department of Homeland Security, XML.Com, Transportation Class Hierarchy

9 9 Ontology-Driven Information Systems Methodology Side – the adoption of a highly interdisciplinary approach (means multiple CoPs and effective coordination mechanisms): –Analyze the structure at a high level of generality. –Formulate a clear and rigorous vocabulary. Architectural Side – the central role in the main components of an information system: –Information resources. –User interfaces. –Application programs. Source: Nicola Guarino, Formal Ontology and Information Systems, Proceedings of FOIS 98, Trento, Italy, 6-8 June 1998.

10 10 Categories of Ontologies Source: Netcentric Semantic Linking (Mapping): An Approach for Enterprise Semantic Interoperability, Mary Pulvermacher, et. Al. MITRE, October SUMO HL7 RIM FEA-RMO* EON SNOMED CT LOINC Examples

11 11 Upper Ontologies –Several examples of Upper Ontologies illustrate their important functionality in practice; SUMO, DOLCE, Omega, MSO for example. –The Suggested Upper Merged Ontology (SUMO) and its domain ontologies form the largest formal public ontology in existence today. –They are being used for research and applications in search, linguistics and reasoning. SUMO is the only formal ontology that has been mapped to all of the WordNet lexicon. –SUMO is written in the SUO-KIF language. –SUMO is free and owned by the IEEE. –The ontologies that extend SUMO are available under GNU General Public License. Adam Pease, a member of the Ontolog Forum, is the Technical Editor of SUMO. –See

12 12 FEA Reference Model Ontology FEA-RMO* The purpose of FEA-RMO is to: –Define an ontology based on FEA reference models (PRM, BRM, SRM, TRM, and DRM), –Develop a common vocabulary, or lexicon, from the FEA reference models, –Support execution, validation, and inference based on FEA reference models, –Support the GSA role in e-Government as market maker, and –Support OMB/AIC partnership in AIC Task 1 & AIC Task 4 by providing lessons learned and an ontology. Source: GSA FEA Reference Model Ontology: A Domain Specific Parsimonious Ontology, Rick Murphy, Enterprise Architect, Office of the CIO, GSA, January 18, 2005.

13 13 Business Reference Model Taxonomy Four Business Areas-one of which is: –Services to Citizens, which has 19 Lines of Business-one of which is: –Health, which has 5 Topics: –Health Care Services –Illness Prevention –Immunization Management –Public Health Monitoring –Consumer Health and Safety "The Business Reference Model is a function-driven framework for describing the business operations of the Federal Government independent of the agencies that perform them. Federal Enterprise Architecture Program Management Office

14 14 Health Domains Taxonomy Access to Care –Focuses on the access to appropriate care Population Health and Consumer Safety –Assesses health indicators and consumer products as a means to protect and promote the health of the general population Health Care Administration –Assures that federal health care resources are expended effectively to ensure quality, safety, and efficiency Health Care Delivery Services –Provides and supports the delivery of health care to its beneficiaries Health Care Research and Practitioner Education –Fosters advancements in health discovery and knowledge Source: Introduction to the Federal Health Architecture Development Methodology, Briefing to the FHA APRG, February 10, 2005.

15 15 Health Domains Taxonomy Health Care Domains (simplified): –Access –Information –Administration –Delivery Services –Research and Education FHA Organization (New): –Regional Initiatives –Clinical Practice –Population Health –Health Interoperability –Federal Health Architecture Source: Architectural Peer Review Group (APRG) Initial Meeting, February 10, 2005

16 16 Demonstrators Samson W. Tu, Stanford Medical Informatics, Stanford University: –The EON and ATHENA Projects –What the Clinician Sees –Guideline-Based Decision Support Architecture –The Ontology in Protégé Peter Yim, Co-Organizer of the Ontolog Forum: –The SUMO Time-Related Concepts in Protégé –Upper Ontology Component Reuse: Extend From an Upper Ontology (SUMO), Rather Than Repeating the Definitions Within the Domain Ontology (HL7)

17 17 Demonstration #1 Health Care Domain EON: A domain-independent, component-based architecture for automation of protocol-based care. Architecture (see next slide): –Problem solving components that have task-specific functions: Planning patients therapy Determining patients eligibility for protocols –A temporal data mediator that Extends the standard relational model with a model of time Supports valid-time temporal queries and updates –A shared knowledgebase of protocols and general medical concepts Source:

18 18 Demonstration: #1 Architecture Clients Servers Protocol Eligibility Checker Therapy Advisory Server Protégé Temporal Mediator Yenta Eligibility Client Yenta Advisory Client Clients Patient Database Protégé Knowledge Base EON Guideline Ontology Medical Domain Ontology Patient Data Model Guidelines

19 19 Pause for Slides & Online Demo Samsons 13 slides follow this slide

20 20 Reuse and Semantic Interoperability Multiple working groups shouldn't redefine-basic concepts –Undermines semantic interoperability across domains and systems –Varying quality of individual models –Limits downstream extensibility Ontology-based formalizations offer more rigor –Typically leverage work of broader community of interests –Designed for reuse and extensibility –Generally reflect more thorough, higher-quality modeling –Reuse of Upper and Mid-level ontologies improves semantic alignment of Domain-Level ontologies and resulting implementations

21 21 Examples of Time Formalization HL-7* –Time taxonomy fragment –TimingEvent model WordNet –Time (Verb) –Time (Noun) SUMO –Process Examples *See Patrick Cassidy notes:

22 22 HL-7 Timing Event Model TypeConcept IDMnemonicDescription L: (AC)10708ACBefore meal (from lat.ante cibus) L: (ACD)10712ACDBefore lunch (from lat.ante cibus diurnus) L: (ACM)10711ACMBefore breakfast (from lat. Ante cibus matutinus) L: (ACV)10713ACVBefore dinner (from lat. Ante cibus vespertinus) L: (HS)10707HSThe hour of sleep (e.b., H18-22) L: (IC)10710ICBetween meals (from lat. Inter cibus) L: (ICD)10718ICDBetween lunch and dinner L: (ICM)10717ICMBetween breakfast and lunch L: (ICV)10719ICVBetween dinner and the hour or sleep L: (PC)10709PCAfter meal (from lat. post cibus) L: (PCD)10715PCDAfter lunch (from lat. post cibus diurnus) L: (PCM)10714PCMAfter breakfast (from lat. post cibus matutinus) L: (PCV)10716PCVAfter dinner (from lat. Post cibus vespertinus)

23 23 Time Representation in HL7 The following was selected from the HL-7 taxonomy: DataTypeDataValue DataTypeInterval –DataTypeIntervalOfPhysicalQuantities –DataTypeIntervalOfPointsInTime DataTypeEventRelatedInterval DataTypeGeneralTimingSpecification DataTypePeriodicIntervalOfTime DataTypeQuantity –DataTypePhysicalQuantity DataTypeParametricProbabilityDistributionOfPhysicalQuantities –DataTypePointInTime

24 24 WordNet Time (Verb) 1.S: (v) clock, time (measure the time or duration of an event or action or the person who performs an action in a certain period of time) "he clocked the runners" 2.S: (v) time (assign a time for an activity or event) "The candidate carefully timed his appearance at the disaster scene" 3.S: (v) time (set the speed, duration, or execution of) "we time the process to manufacture our cars very precisely" 4.S: (v) time (regulate or set the time of) "time the clock" 5.S: (v) time (adjust so that a force is applied and an action occurs at the desired time) "The good player times his swing so as to hit the ball squarely From

25 25 WordNet Time (Noun) 1.S: (n) time, clip (an instance or single occasion for some event) "this time he succeeded"; "he called four times"; "he could do ten at a clip" 2.S: (n) time (an indefinite period (usually marked by specific attributes or activities)) "he waited a long time"; "the time of year for planting"; "he was a great actor is his time" 3.S: (n) time (a period of time considered as a resource under your control and sufficient to accomplish something) "take time to smell the roses"; "I didn't have time to finish"; "it took more than half my time" 4.S: (n) time (a suitable moment) "it is time to go" 5.S: (n) time (the continuum of experience in which events pass from the future through the present to the past) 6.S: (n) clock time, time (the time as given by a clock) "do you know what time it is?"; "the time is 10 o'clock" 7.S: (n) fourth dimension, time (the fourth coordinate that is required (along with three spatial dimensions) to specify a physical event) 8.S: (n) time (a person's experience on a particular occasion) "he had a time holding back the tears"; "they had a good time together" 9.S: (n) meter, metre, time (rhythm as given by division into parts of equal duration) 10.S: (n) prison term, sentence, time (the period of time a prisoner is imprisoned) "he served a prison term of 15 months"; "his sentence was 5 to 10 years"; "he is doing time in the county jail"

26 26 Time Representation in SUMO WordNet N1 maps to SUMO Process Appearance as argument number 1 –documentation Process "Intuitively, the class of things that happen and have temporal parts or stages. Examples include extended events like a football match or a race, actions like Pursuing and Reading, and biological processes. The formal definition is: anything that lasts for a time but is not an Object. Note that a Process may have participants 'inside' it which are Objects, such as the players in a football match. In a 4D ontology, a Process is something whose spatiotemporal extent is thought of as dividing into temporal stages roughly perpendicular to the time-axis. –subclass Process Physical From

27 27 SUMO Process Examples Appearance as argument number 2 (1 of 5) –subclass DualObjectProcess Process dual object process is a subclass of process Appearance as argument number 3 (1 of 27) –domain agent 1 Process the number 1 argument of agent is an instance of process

28 28 SUMO Process Examples Antecedent (1 of 4) (=> (and (instance ?PROC Process) (subProcess ?SUBPROC ?PROC)) (exists (?TIME) (time ?SUBPROC ?TIME))) –if ?PROC is an instance of process and ?SUBPROC is a subprocess of ?PROC, then there exists ?TIME so that ?SUBPROC exists during ?TIME

29 29 Questions to consider Ontolog groups focus on interoperability needs of NHIN & FHA architects, –How should ontologies enable the interoperability of patient health records? –How should existing and prospective health domain ontologies and taxonomies be aligned with upper ontologies to improve the accuracy of conceptual information transfer ? –Especially among systems using different domain knowledge representations.

30 30 Next Steps for Federal Architects Help with the Definitions and Relationships for the Health Domains –so they connect between the FEA Reference Model Ontology and Domain-Specific Ontologies. Help with Reuse of Upper Ontology Components in Domain Ontologies. Identify Other Examples of Domain Ontologies and Ontology-Driven Information Systems That Will Serve as Best Practice Examples.

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35 35 Mark Musen, MD, PhD Mark Musen, MD, PhD: –Professor of Medicine and, by courtesy, Computer Science, Stanford University School of Medicine. –Dr. Musen's research interests include knowledge modeling in biology and medicine, knowledge management, automated support for clinical-practice guidelines and for clinical trials, and knowledge-based approaches to public-health surveillance. – html

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