New York State Center of Excellence in Bioinformatics & Life Sciences R T U MIE 2006 Tutorial Standards and Ontology Part 3: SNOMED - CT Sunday August 27th, 2006 Werner Ceusters, MD Ontology Research Group Center of Excellence in Bioinformatics & Life Sciences SUNY at Buffalo, NY

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Objectives Use the history of the development of SNOMED- CT to show the differences between a terminological and an ontological approach; Look at some problematic features of concept- based thinking and how they are addressed in SNOMED-CT; Make you think “ontologically”  ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U The “real” MIE2006 SNOMED-CT Tutorial logical definitions; integrated broad content; table structures; interaction between the relational model (three core tables) and the concept model used to represent medical content (hierarchies and relationship types linking the hierarchies); related mappings; support for data recording, retrieval and analysis; quality assurance processes; highlights of the SNOMED CT January 2006 release. Kent A Spackman Sunday, 27 August 2006; – TutorialSpackman.pdf

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Presentation outline Ontology versus Terminology Terminological thinking in SNOMED: –From SNOP to SNOMED-CT Mistakes in SNOMED-CT due to terminological thinking How to make SNOMED-CT better using real ontology

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminology

New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Terminology’ has two meanings 1)The discipline of terminology management –synonymous with terminology work (used in ISO 704) 2)The set of designations used in the special language of a subject field, such as the terminology of medicine –Used in in both the singular and plural –Used with an article in the singular: a terminology

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminology is VERY standardised ISO 704: 2000Terminology work – Principles and methods ISO 860: 1996Terminology work – Harmonization of concepts and terms ISO : 2000Terminology work – Vocabulary – Part 1: Theory and application ISO 15188: 2001Project management guidelines for terminology standardization ISO :2000Terminology work – Vocabulary – Part 2: Computer applications ISO 12620: 1999Computer applications in terminology – Data categories ISO 16642: 2003Computer applications in terminology – Terminological markup framework ISO 2788: 1986Documentation – Guidelines for the establishment and development of monolingual thesauri ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U ISO Standards in Terminology: building blocks Objects perceived or conceived, concrete or abstract abstracted or conceptualized into concepts Concepts depict or correspond to a set of objects based on a defined set of characteristics represented or expressed in language by designations or by definitions organized into concept systems Designations represented as terms, names (appellations) or symbols designate or represent a concept attributed to a concept by consensus within a special language community ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Fundamental Activities of Terminology Work Identifying ‘concepts’ and ‘concept relations’; –Analyzing and modeling concept systems on the basis of identified concepts and concept relations; –Establishing representations of concept systems through concept diagrams; –Crafting concept-oriented definitions; –Attributing designations (predominantly terms) to each concept in one or more languages; and, –Recording and presenting terminological data, principally in terminological entries stored in print and electronic media (terminography).

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Origin: Peirce, Ogden & Richards, Wüster Unit of Thinking (Concept) Designation (Symbol, Sign, Term, Formula etc.) Referent (Concrete Object, Real Thing, Conceived Object) (Unit of Thought, Unit of Knowledge)

New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘cancer’ in SNOMED-CT

New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Cancer’ as disease versus morphology

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Why terminologies ? As such ? –Fixing/stabilizing the language within a domain and a linguistic community; –Unambiguous communication. In Healthcare Information Technology ? –Semantic Indexing; –Information exchange and linking between heterogeneous systems; –Terminologies as basis for documentation through coding and classification

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Coding versus classification Coding: –Annotate terms in the EHR with codes from a coding system  synonyms, translations, hierarchies Classification: –Assign patients exhibiting certain features to a predefined class  purpose oriented, culture dependent Frequently mixed up !

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Fractured nose = Fracture of nose ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Coding / classification confusion “patient with fractured nose” = “patient with fracture of nose” But therefor not “fractured nose” = “fracture of nose” !

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology Ontology: the science of what things exist and how they relate to each other An ontology is a representation of some pre-existing domain of reality which –(1) reflects the properties of the objects within its domain in such a way that there obtains a systematic correlation between reality and the representation itself, –(2) is intelligible to a domain expert –(3) is formalized in a way that allows it to support automatic information processing OWL (DLs) does only this bit !

New York State Center of Excellence in Bioinformatics & Life Sciences R T U A division of labour Terminology: –Communication amongst humans –Communication between human and machine Ontology: –Representation of “reality” inside a machine –Communication amongst machines –Interpretation by machines

New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED-CT

New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED-CT SNOMED CT is a comprehensive and precise clinical reference terminology that makes healthcare information accessible and useable, whenever and wherever it is needed. Global in scope, yet adaptable for national purposes, SNOMED CT provides a “common language” of unsurpassed depth that enables a consistent way of capturing, sharing and aggregating health data across clinical specialties and sites of care.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED International

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Milestones in the development of SNOMED SNOP – 1965 SNOMED – 1974 SNOMED II – 1979 SNOMED Version 3.0 – 1993 LOINC codes integrated into SNOMED – 1997 SNOMED Version 3.5 – 1998 SNOMED RT – 2000 SNOMED CT (SNOMED RT + CTV3) – First release January 2002 SNOMED CT Spanish Edition – April 2002 SNOMED CT German Edition - April 2003 Last version: SNOMED-CT English – July 2006

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Systematized Nomenclature of Pathology (1965) Author: –CAP Committee on Nomenclature and Classification of Disease 4 coding axes: –Topography (physical/natural features), –Morphology (structure/form), –Etiology (causes), and –Function

New York State Center of Excellence in Bioinformatics & Life Sciences R T U 4 Hierarchies of SNOP Topography T-terms –names of body sites Morphology M-terms –names of structural changes that occur in tissues as a result of disease Etiology E-terms –causative agents of disease (chemicals, bacteria, viruses) FunctionF-terms –names of the physiological manifestations associated with disease (also symptoms and some viral diseases) ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Standard’ SNOP statement In ‘TMEF-form’: ‘The body site T has undergone morphological change M due to the causative agent E resulting in physiological manifestations F’. Or more accurate: [Morphology] in [Topography] caused by [Etiology] leads to [Function]

New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOP example statement [M: inflammation] in [T: lung] caused by [E: Influenza virus] leads to [F: hypoxia] ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED International (1995, V3.1) TTopography12,385 MMorphology 4,991 FFunction16,352 LLiving Organisms24,265 CDrugs & Biological Products14,075 APhysical Agents, Forces and Activities 1,355 DDisease/ Diagnosis28,623 PProcedures27,033 SSocial Context 433 JOccupations 1,886 GGeneral Modifiers 1,176 TOTAL RECORDS 132,641 ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Merriam-Webster On-Line Dictionary on ‘diagnosis’ 1a:the art or act of identifying a disease from its signs and symptoms 1b:the decision reached by diagnosis 2:a concise technical description of a taxon 3a:investigation or analysis of the cause or nature of a condition, situation, or problem 3b:a statement or conclusion from such an analysis.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Hierarchical structure of Snomed International posterior anatomic leaflet mitral cardiac valve cardiovascular T ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED International’s Hierarchical organization: an example Bone Long Bone Periosteum Shaft ? Isa Part or adjacency ? Part of

New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED International: Multiple ways for expressing the same entities D Acute appendicitis, NOS D Appendicitis, NOS G-A231Acute M-41000Acute inflammation, NOS G-C006In T-59200Appendix, NOS G-A231Acute M-40000Inflammation, NOS G-C006In T-59200Appendix, NOS

New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED-RT: first attempt to make relationships explicit

New York State Center of Excellence in Bioinformatics & Life Sciences R T U D : ‘Postoperative esophagitis’ In SNOMED III –Parent term in the hierarchy:D Esophagitis, NOS –Cross-reference field :(T-56000)(M-40000)(F-06030) where: T Esophagus M Inflammation F Post-operative state In SNOMED-RT (in KRSS syntax) –(defconcept D (and D –(some assoc-topography T-56000) –(some assoc-morphology M-40000) –(some assoc-etiology F-06030))) Spackman KA, Campbell KE, Cote RA. SNOMED RT: a reference terminology for health care. Proc AMIA Annu Fall Symp. 1997;: ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U KRSS: an old ‘description logics’ Description logics: A decidable fragment of FOL A propositional modal logic A classes and properties (concepts and roles) oriented KR language Subsumption and satisfiability (consistency) are the key inferences Most DLs are supersets of ALC –Boolean operators on concepts –Existential and Universal quantifiers OWL-DL is a large superset (SHOIN): –Property hierarchies & Transitive roles (SH) –Inverse (I) –Nominals (O) (hasValue and one of) –Number restrictions (counting quantifiers)

New York State Center of Excellence in Bioinformatics & Life Sciences R T U 2002: SNOMED-CT A merger between –SNOMED-RT, and –the England and Wales National Health Service's Clinical Terms (previously known as the Read Codes). SNOMED CT is considered to be the first international terminology.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED-CT hierarchies Body structure Clinical finding Context-dependent category Environments and geographical locations Event Linkage concept Observable entity Organism Pharmaceutical / biologic product Physical force Physical object Procedure Qualifier value Record artifact Social context Special concept Specimen Staging and scales Substance ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Clinical Finding hierarchy Administrative statuses Adverse incident outcome categories Clinical history and observation findings Clinical stage finding Deformity Disease (disorder) Drug action Edema Effect of exposure to physical force Finding by method Finding by site Finding of grade Finding related to physiologic substance Finding reported by subject or history provider General clinical state finding Neurological finding Prognosis/outlook finding Sequelae of external causes and disorders Wound finding ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED-CT evolution

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Description of Algorithms Ceusters W, Smith B, Kumar A, Dhaen C. Mistakes in Medical Ontologies: Where Do They Come From and How Can They Be Detected? in: Pisanelli DM (ed) IOS Press, Studies in Health Technology and Informatics, vol 102, 2004.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Exploiting Lexical, semantic and conceptual relations gall gallbladder urinary bladder gall gall bladder bladder inflammation urine urinary bladder inflammation cystitis gallbladder inflammation biliary cystitis inflammation gallbladder inflammation urinary bladder

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Find the mistakes

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Undetected synonymy ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Undetected synonymy ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Inadequate homonymy ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mistakes due to inappropriate lexical mapping ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Total / partial inconsistencies ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Wrong manually created subsumption ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Wrong manually created subsumption ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Wrong computed subsumption SNOMED-RT (2000) SNOMED-CT (2003) ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Missed subsumption ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mereological errors ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Improper negation handling ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Take off of ontology in biomedical informatics Concept/terminology-based systems make implicit knowledge explicit Ontologies aim to push explicitness further: –reasoning by machines Classification Prediction Triggering of alerts

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusion Concept-based terminology (and standardisation thereof) is there as a mechanism to improve understanding of messages by humans. It is NOT the right device –to explain why reality is what it is, how it is organised, etc., (although it is needed to allow communication), –to reason about reality, –to make machines understand what is real, –to integrate across different views, languages, conceptualisations,...

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Why not ? Because there is no valid benchmark !

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Why not ? Does not take care of universals and particulars appropriately Concepts not necessarily correspond to something that (will) exist(ed) –Sorcerer, unicorn, leprechaun,... Definitions set the conditions under which terms may be used, and may not be abused as conditions an entity must satisfy to be what it is Language can make strings of words look as if it were terms –“Middle lobe of left lung”

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Today’s biggest problem: a confusion between “terminology” and “ontology” The conditions to be agreed upon when to use a certain term to denote an entity, are often different than the conditions which make an entity what it is. –Trees would still be different from rabbits if there were no humans to agree on how these things should be called. “ontos” means “being”. The link with reality tends to be forgotten: one concentrates on the models instead of on the reality.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U For example: some SNOMED definitions The SNOMED Glossary: –Concept: A clinical idea to which a unique ConceptID has been assigned in SNOMED CT. Each Concept is represented by a row in the Concepts Table. The SNOMED-CT User Manual: –Concepts are unique units of thought. –Disorders are concepts in which there is an explicit or implicit pathological process causing a state of disease which tends to exist for a significant length of time under ordinary circumstances. ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U What to do about it ? (1) Research: –Revision of the appropriatness of concept-based terminology for specific purposes –Relationship between models and that part of reality that the models want to represent –Adequacy of current tools and languages for representation –Boundaries between terminology and ontology and the place of each in semantic interoperability in healthcare

New York State Center of Excellence in Bioinformatics & Life Sciences R T U What to do about it ? (2) Training and awareness –Make people more critical wrt terminology and ontology promisses What is needed must be based on needs, not on the popularity of a new paradigm But in a system, it’s not just your own needs, it is each component’s needs ! –Towards “an ontology of ontologies” First description Then quality criteria

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology based on Unqualified Realism Accepts the existence of –a real world outside mind and language –a structure in that world prior to mind and language (universals / particulars) Rejects nominalism, conceptualism, ontology as a matter of agreement on ‘conceptualizations’ Uses reality as a benchmark for testing the quality of ontologies as artifacts by building appropriate logics with referential semantics (rather than model-theoretic)

New York State Center of Excellence in Bioinformatics & Life Sciences R T U 3 fundamentally different in levels 1.the reality on the side of the patient; 2.the cognitive representations of this reality embodied in observations and interpretations on the part of clinicians and others; 3.the publicly accessible concretizations of such cognitive representations in representational artifacts of various sorts, of which ontologies and terminologies are examples.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U From concepts to universals Unit of Thinking (Concept) Designation (Symbol, Sign, Term, Formula etc.) Referent (Concrete Object, Real Thing, Conceived Object) (Unit of Thought, Unit of Knowledge) ~ Universal ??? Universal Particular

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relevance for EHR & Semantic Interoperability REALITYREALITY BELIEFBELIEF Ontology EHR The conceptualist approach

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relevance for EHR & Semantic Interoperability REALITYREALITY Ontology EHR The realist approach L O G O L K A I S N S G

New York State Center of Excellence in Bioinformatics & Life Sciences R T U What should be done with SNOMED-CT concretely to make it a good standard ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U 1. Adhere to a consistent upper ontology grounded in realism (BFO) Differentiate between: –Particulars – universals Thus not: Belgium isa European Country isa WEU country –Occurrents – continuants Thus not: vomiting isa disease –Unless all diseases would be occurrents –Dependents – independents fracture of nasal bones - fractured nasal bones Build relationships that take these distinctions into account

New York State Center of Excellence in Bioinformatics & Life Sciences R T U 2. Distinguish what is ontological, from what is epistemological, from what is pragmatic Thus not (in the ontology !): –Notable event isa event –Seriously reportable event isa event –Disease of presumed infectious origin (disorder) –Iatrogenic disease (disorder)

New York State Center of Excellence in Bioinformatics & Life Sciences R T U 3. Make a sound upper domain ontology Requires good description of what is –Disease / disorder / illness –Symptom / sign –Normal / canonical / variant / pathological –Observation –Procedure / action –…

New York State Center of Excellence in Bioinformatics & Life Sciences R T U 4. Document reason for changes in SNOMED 1.changes in the underlying reality does the appearance of an entry (in a new version of an ontology or in an EHR) relate to the appearance of an entity or a relationship among entities in reality ?; 2.changes in our (scientific) understanding; 3.reassessments of what is considered to be relevant for inclusion (notion of purpose), or: 4.encoding mistakes introduced during data entry or ontology development.