Hans ÅhlfeldtföreläsningNationell termkonferens 2013

Hans Åhlfeldt 1955 - 2010

Medical Terminology:Should we care about

ontology?

Stefan SchulzMedical

University of Graz(Austria)

purl.org/steschu

Hans Åhlfeldt LectureSwedish Terminology Conference, 14 Oct 2013

Objectives of the talk

� To demonstrate facets of "meaning" in health care

and biomedical science from a formal ontology

viewpoint

� To outline the scope of "models of meaning"

(terminologies, thesauri, classifications, ontologies,

information models)

� To analyze the impact of formal ontology on medical

terminology management and mapping between

different terminology systems in general

� To highlight mapping problems and challenges of

current terminologies in particular

Case study: Breast cancer

Initial signs and symptoms

� Marie, 42 years old,

reports small lump in left breast

(self exam).

� General Practitioner:

� "cherry-sized painless lump in upper left quadrant of left

breast"

� no previous history of neoplasms

� referral to specialist: " breast CA ? "

Confirmation of diagnosis

� Specialist:

� history of "breast nodes": fibroadenoma?

� family history of breast cancer (mother, diagnosed at 51, total

hemimastectomy, brain metastases,

death with 59)

� palpation: painless lump (1cm)

� Routine lab: no abnormalities

� Mammogram:

suggestive of malignancy

� scheduled for lumpectomy

Therapy + follow up

� Surgical removal of lump (1.3cm) from left breast

� Histology: invasive ductal carcinoma, HER2+

� ICD: C50.4, ICD-O: M8500

� TNM: T1N0M0 (0.7 cm)

� Plan

� chemotherapy: monoclonal antibody

trastuzumab (Herceptin) , 1y

� echocardiography screening

� Patient Information

� 5y Survival rate: Stage I: 88%

� known drug side effect: heart disease

Background knowledge

Literature search: monoclonal antibodies and cancer therapy

Semantic annotations

Gene product annotations using Gene ontology

Additional facts

� The monthly cost of Herceptin is $4,500

� Herceptin is produced by Roche

� Herceptin has global sales of 5.25 billion SFr in 2011

� Clinical trials (HERA, PHARE): One year on Herceptin is best

� Marie C. got heart failure after being treated with Herceptin

Analyzing meaning: representations of meaning using

a wealth of biomedical vocabularies

• ICD: C50.4 Neoplasm of upper-outer quadrant of breast• ICD-O:M8500 Invasive ductal carcinoma• TNM:T1N0M0 Tumor 1.0 cm or less• SCT:392021009 Lumpectomy of breast• SCT:387003001 Trastuzumab• LOINC:48676-1 HER2 in Tissue• MeSH:D000911 Antibodies, Monoclonal• MeSH:D009369 Neoplasms• GO:0007569 Cell aging• GO:0006281 DNA repair• GO:0005634 Nucleus• UniProt:P38398 Breast cancer type 1 susceptibility protein

More than just codes: discourse context!

� no previous history of neoplasms

� history of "breast nodes"

� family history of breast cancer (mother)

� lab: no abnormalities

� Mammogram suggestive of malignancy

� Patient: scheduled for lumpectomy

� Clinical evidence: monoclonal antibodies useful for advanced breast cancer

� Treatment plan: Trastuzumab

� Patient informed about known side effect: heart disease

� Clinical trials suggest that one year on Herceptin is best

Models of meaning

Coding systems and models of meaning

� Coding systems cover most concepts in health care and

biomedical research

� Use of codes and coding systems requires analysis of the

underlying models of meaning:

� Terminologies

� Thesauri

� Classifications

� (Formal) Ontologies

� Information models

� Crucial question: which are exactly the things that are

represented: "Ontological Commitment"

Hybrids

Components of Domain Models of Meaning

Nodes and Links

Hierarchies(In)formal definitions

domain or region of DNA [GENIA]:

A substructure of DNA molecule which is supposed to

have a particular function, such as a gene, e.g., c-jun

gene, promoter region, Sp1 site, CA repeat. This class

also includes a base sequence that has a particular

function.

Peptides [MeSH]:

Members of the class of compounds composed of

AMINO ACIDS joined together by peptide bonds

between adjacent amino acids into linear, branched or

cyclical structures. OLIGOPEPTIDES are composed of

approximately 2-12 amino acids. Polypeptides are

composed of approximately 13 or more amino acids.

PROTEINS are linear polypeptides that are normally

synthesized on RIBOSOMES.

19429009|chronic ulcer of skin|116680003|is a|=64572001|disease|{116676008|associated morphology|=405719001|chronic ulcer|363698007|finding site|=39937001|skin structure|}

Natural language descriptions

•Benign neoplasm of heart

•Benign tumor of heart

•Benign tumour of heart

•Benign cardiac neoplasm

•Gutartiger Herzumor

•Gutartige Neubildung am

Herzen

•Gutartige Neubildung: Herz

•Gutartige Neoplasie des

Herzens

•Tumeur bénigne cardiaque

•Tumeur bénigne du cœur

•Neoplasia cardíaca benigna

•Neoplasia benigna do

coração

•Neoplasia benigna del

corazón

•Tumor benigno do corazón

• theory of reality • theory of meaning of (human language) designations

• theory of knowledge

Ontology Semantics

Epistemology

bla bla bla

Three kinds of models of meaning

• Theories that attempt to give precise

mathematical formulations of the

properties and relations of certain

entities.

(Stanford Encyclopedia of Philosophy)

• Set of terms representing the system

of concepts of a particular subject

field.

(ISO 1087)

• Artefacts in which information is recorded

A. Rector, SemanticHealth D6.1

Ontologies Terminologies

Information models

Three kinds of models of meaning

Quine O. On what there is. In: Gibson R. Quintessence - Basic Readings from the Philosophy of W. V. Quine. Cambridge: Belknap Press, Harvard University, 2004.Schulz S, Jansen L. Formal ontologies in biomedical knowledge representation. Yearb Med Inform. 2013;8(1):132-46.

Formal descriptions

• MRSA subclassOf SA

• SA subclassOf Staphylococcus

• SA subclassOf bearerOf some 'MR quality'

Textual descriptions

• “MRSA is defined as SA for which methicillin has

no toxic effect”

• Concept 1 – Synonyms:

-SA

-Staphylococcus aureus

-Staph. aur.

- Concept 2 – Synonyms:

-MRSA

-Methicillin-resistant SA

-Methicillin-resistant Staphylococcus Aureus

Methicillin resistance

Ontologies Terminologies

Information models�Clinically confirmed

�Confirmed by antibiogram

�Suspected

�None

�Unknown

�

Three kinds of models of meaning

Ontology Terminology

Information models

Clear boundaries only in theory!

Ontology

Information models

Clear boundaries only in theory!

Information models

SNOMED CT

HL7RIM

ICD 10

openEHR EN 13606

TerminologyMeSH

Formal descriptions

• MRSA subclassOf SA

• SA subclassOf Staphylococcus

• SA subclassOf bearerOf some 'MR quality'

Textual descriptions

• “MRSA is defined as SA for which methicillin has

no toxic effect”

• Concept 1 – Synonyms:

-SA

-Staphylococcus aureus

-Staph. aur.

- Concept 2 – Synonyms:

-MRSA

-Methicillin-resistant SA

-Methicillin-resistant Staphylococcus Aureus

Methicillin resistance

Ontologies Terminologies

Information models�Clinically confirmed

�Confirmed by antibiogram

�Suspected

�None

�Unknown

�

Let's begin with terminologies

Human Language vs. Entities of the world

Terms

Entity Types

Entities of

the World

„breast cancer“

"Mammakarzinom"

"bröstcancer"

"cancer du sein"

Instance_of

Universals, classes,

(Concepts)

abstract

concrete

Particulars,

instances

repr

esen

t

represent

Marie C's breast

cancer

The type

“breast cancer”

Shared meanings(concepts)

…are stored in dictionaries

and represented by

terminologies / thesauri• quasi-synonyms / translations• broader meaning• narrower meaningterminologies / thesauri

sufficient, e.g. for document

retrieval, example MeSH

Terms„breast cancer“

"Mammakarzinom"

"bröstcancer"

"cancer du sein"

Entities of

the World

Database systems / information models

store references to…

Information

entity is about

… are organized in formal ontologies

Entity Types

(Formal) Ontology in a nutshell

� Formal ontologies are based on taxonomies, which

relates types and subtypes (classes and subclasses):

� Breast Cancer is a Cancer equivalent to:

�All instances of Breast Cancer are instances of Cancer

(at all times without exceptions)

� Relations:

� instance_of relates individuals with types

� is_a or subClassOf relates types / classes

� Others like part_of relate individuals

class-class relations have to be defined in terms of relations

between individuals

Ontological framework for entity types

Type / Subtype Hierarchy (Taxonomy)

Tumor

of Breast

Breast

Cancer

Benignant

Tumor of

Breast

Malignant

Disorder

Is_a Is_a Is_a

� Formal ontologies are based on taxonomies, which

relates types and subtypes (classes and subclasses):

� Breast Cancer is a Cancer equivalent to:

�All instances of Breast Cancer are instances of Cancer

(at all times without exceptions)

� Relations:

� instance_of relates individuals with types

� is_a or subClassOf relates types / classes

� Others like part_of relate individuals

class-class relations have to be defined in terms of relations

between individuals

Ontological framework for entity types

Formal Ontology

Type and its extensions into the real world

Domain

Formal Ontology

Type and its extensions into the real world

Cancer

Domain

Type - class isomorphism

Breast Cancer

Cancer

Is_a

ColonCancer

ProstateCancer

Formal Ontology

Domain

Type - class isomorphism

Breast Cancer

Cancer

Is_a

Formal Ontology

Domain

Relations and Definitions

BreastBreast Cancer

Cancer

Is_a

Formal Ontology

Domain

Anatomical Object

Is_a

Relations and Definitions

Breasthas-location

someBreast Cancer

Cancer

Is_a

Formal Ontology

Domain

Anatomical Object

Is_a

Relations and Definitions

Breasthas-location

someBreast Cancer

Cancer

Is_a

HER+ Breast Cancer

HER2+

Protein

Is_a

includes some

Formal Ontology

Domain

Anatomical Object

Is_a

Languages for formal ontologies

� Natural Language

� Logic

� First order:

� Description Logics, e.g. OWL-DL:

∀x,t: instanceOf (x, BreastCancer, t) �

instanceOf (x, Cancer) ∧

∃y: instanceOf(y, Breast) ∧ hasLocation(x,y,t)

BreastCancer subClassOf Cancer and

hasLocation some Breast

“Every breast cancer is a cancer that is located in some breast”

Logic is computable: it supports machine inferences but…

…. it only scales up if it has a very limited expressivity

…. it does not allow for exceptions

Strengths of Formal Ontologies

� Exact, logic-based descriptions of entity types that are instantiated by real-world objects, processes, qualities etc.

� Representation of stable, context-independent accounts of reality

� Independence from human language

� Use of formal reasoning methods using tools and approaches from the AI / Semantic Web community

� Description logics (e.g. OWL-DL) as a mature representation formalism: simplified, but mostly sufficient view of the world:

� Classes (as extensions of types)

� Instances� Relations

� Axioms / Constraints

Ontology exercise: is this taxonomy correct?

Why ontology matters for terminology (I)

� Increasingly biomedical terminologies are developed as

terminology / ontology hybrids, e.g. SNOMED CT, future ICD

versions, Gene Ontology

� Ontological foundation used for automated computation of

taxonomic links, see SNOMED CT

� Ontologies enforce precise definition of the meaning of terms

� Problem; unawareness of the principal differences between

terminologies and ontologies

� Risks: construction of inappropriate ontologies out of

terminologies

� Example: NCIT

Why ontology matters for terminology (II)

� Facilitates mapping between different terminology systems

� Lexical mapping

� A = B if the meaning of A in natural language is (nearly) the same as the

meaning of B in most discourse contexts

� Problem: no clear matching criterion, analysis of the hierarchical context,

identity of strings not necessarily identity of meaning

� Ontology-based mapping

� Ideally clear formal definitions about class membership

� Use of formal reasoning for verification

� Use case: SNOMED CT – WHO classifications

� Currently investigated by IHTSDO – WHO JAG

Ontological and terminological aspects of SNOMED CT

SNOMED CT - clinical terminology with

ontological foundations

� Terminology for clinical

data covering diseases,

findings, procedures,

organisms, substances etc.

� 311, 000 concepts,

connected by 1,360,000

relational expressions

� ontology-based

"terminological standard"

� Description Logics EL:� restricted to: equivalence, subsumption , existential role

restriction, conjunction

� allows matching of equivalent expressions

SNOMED CT as a terminology

links medical terms including synonyms

and translations to language-independent

concepts

z.Zt.

311 000

concepts

732 000

engl. terms

SNOMED CT as a formal ontology

hierarchies:

strict

specialization

(subclass-of)

SNOMED CT as a formal ontology

restrictions based on simple logics:

C1 – Rel – C2 interpreted as:

FOL: ∀x: instanceOf(x, C1) ⇒

∃y: instanceOf(C2) ∧ Rel(x,y)

DL: C1 subclassOf Rel some C2

Relations (Attributes): z.B.Associated morphology

Finding site

(50 relation types)

Why SNOMED CT has to clarify its "ontological commitment"

� Ontological commitment: “Agreement about the ontological

nature of the entities being referred to by the representational

units in an ontology”

� Formal ontologies: subsumption and equivalence statements

are either true or false

� Problem: change of truth-value of axioms and sentences

according to resulting competing interpretations

Nicola Guarino (1998). "Formal ontology and information systems". Formal Ontology in Information Systems: Proceedings of the First International Conference (FIOS'98), June 6-8, Trento, Italy.

http://iwannabeadr.com/

Pulmonicvalve

stenosis

Tetralogy of Fallot

http://iwannabeadr.com/

Pulmonicvalve

stenosis

Tetralogy of Fallot

Tetralogy of Fallot equivalentTo

Pulmonic Valve Stenosis and

Ventricular Septal Defect and

Overriding Aorta and

Right Ventricular hypertrophy

entails:

e.g.

Tetralogy of Fallot subclassOf

Pulmonic Valve Stenosis

Tetralogy of Fallot subclassOf

Ventricular Septal Defect

etc.

SNOMED CT Example

Proper parts or taxonomic parents ?

subclassOf

Red Light Yellow Light Green LightASD PVS RVH OA

Example from Harold Solbrig

subclassOf

Tetralogy ofFallot

TrafficLight

Extension of “Pulmonic Valve Stenosis” includes extension

of “Tetralogy of Fallot”: FALSE

Alternative interpretation

F

P

P

P

P

F

F

P

F

P

P

Extension of “Patient with Pulmonic Valve Stenosis”includes extension of “Patient with Tetralogy of Fallot”: TRUE

Alternative interpretation

F

P

P

P

P

F

F

P

F

P

P

Extension of “Situation with Pulmonic Valve Stenosis”includes extension of “Situation with Tetralogy of Fallot”: TRUE

Alternative interpretation

SNOMED CT's ontological commitment

� Many hierarchies and definitions SNOMED CT suggest that

SNOMED CT’s ontological commitment is heterogeneous

� SNOMED CT’s alternative commitments are completely

implicit, but they shed light on clinicians’ reasoning

� Use of SNOMED CT as an ontology depends on agreementabout its ontological commitment

� Expert recommendation:

SNOMED CT disorder concepts commit to clinical situations:"A clinical situation with X is a phase of a patient's life in which a

condition of the type X is wholly present"

Review of 400 sample SNOMED CT disorder concepts

� 4 experts: Kent Spackman, Alan Rector, Jean-Marie

Rodrigues, Stefan Schulz

� Results: ~ 11% of disorder concepts represent

situations rather than conditions

� For the rest, both interpretations are possible

� Agreement difficult – fuzzy boundary between what

should be interpreted as a condition and what as a

situation

� Recommendation: all SNOMED CT disorder concepts

should be interpreted as clinical situations

Schulz S, Rector A, Rodrigues JM, Spackman K. Competing Interpretations of Disorder Codes in SNOMED CT and ICD. AMIA 2012

Ontology Terminology

Epistemology

Marie, revisited: Contextual and epistemic elements in

clinical documentation

� Female patient, 45, reports small lump in left breast (self exam).

� General Practitioner:

� "cherry-sized painless lump in upper left quadrant of left breast"

� no previous history of neoplasms

� referral to specialist: "breast CA ? "

� Specialist:

� history of "breast nodes": fibroadenoma?

� family history of breast cancer (mother, diagnosed at 51, total hemimastectomy, brain

metastases, death with 59)

� palpation: painless lump (1cm)

� Routine lab: no abnormalities

� Mammogram: suggestive of malignancy

� scheduled for lumpectomy

� Surgical removal of lump (1.3cm) from left breast, invasive ductal carcinoma, HER2+

� chemotherapy planned: monoclonal antibody trastuzumab (Herceptin) , 1y

� echocardiography screening scheduled

� Information to patient: 5y survival rate: Stage I: 88%, risk of drug side effect: heart disease

Analyzing context

� Both coded content and sentences are context dependent

� Examples: [exists at coding]

� Breast cancer (family history) √√√√

� Breast cancer (hypothesis of GP, motivates referral) ?

� Breast cancer (suspicious due to mammogram) ?

� Breast cancer (confirmed fact after surgery) √√√√

� Neoplasm (negated in previous history) ∅∅∅∅

� chemotherapy (planned treatment) ∅∅∅∅

� heart disorder (risk) ∅∅∅∅

� monoclonal antibody (topic in scientific paper) √√√√

� survival rate (estimated number according to cohort) √√√√

The pitfalls of epistemology

� Content that blends

� the objective nature of things

� the subjective description

� Examples

� plan, suspicion, uncertainty,

risk, negation

� Ideally: Information models

� Main rationale: coding requirements (information models

cannot be taken for granted)

� Beware of literal interpretation of NL head / modifier pairs:

� a prevented pregnancy is not a pregnancy

� a planned tonsillectomy is not a tonsillectomy

SNOMED CT contextual / epistemic branch

Associated procedure Procedure

Procedure context

Context values for actions• Done, not done

• Planned, requested

Associated findingClinical finding; or

Observable / Observation with result

Finding context

Finding context value• Present, absent, possible

• Unknown

• Goal, risk, etcSituation with

explicit context

Subject relationship context

Subject relationship value• Subject of record

• Family member, etc

Temporal context Temporal context value

• Current

• Past, etc

� Example: Suspected pregnancy

� Several interpretations (which are the instances?)

� "Real pregnancies" in a possible world

� (Situations of) Patients about which can be said that they are possible

pregnant

� Information artifacts about the type Pregnancy, modified by "suspected"

� There is no ideal representation in DL

� Current DL axioms in SNOMED CT are flawed

� Tentative solution with value restriction

'Suspected Pregnancy' equivalentTo

'Diagnostic statement' and isAboutSituation only Pregnancy and hasAttribute some Suspected

Interpretation of SNOMED CT context model

Breast Cancer

Structure

Breast Cancer

Process

Breast Cancer

Condition

Patient

EHR

Breast Cancer

Situation

Breast Cancer

Suspected

isAbout onlySituation

includes someCondition

isA

Model according to SemanticHealthNet

SemanticHealthNet: EU Network of Excellence www.semantichealthnet.eu

Suspected

hasAttribute some

Resolution of ontology quiz

Ontology based harmonization SNOMED - ICD

Ontology based harmonization SNOMED - ICD

� Kent Spackman

� Hazel Brear

� Monica Harry

� Jane Millar

� Kristina Persson

� Stefan Schulz

� Harold Solbrig

• 2010: agreement on joint terminology development• Joint Advisory Group WHO - ITDSDO• Common Ontology for SNOMED CT / ICD 11

� Bedirhan Üstün

� Christopher Chute

� Vincenzo Della Mea

� Alan Rector

� Molly M Robinson Nicol

� Jean-M. Rodrigues

� Kim Sukil

E10.0 Mit Koma

E10.1 Mit Ketoazidose

E10.2 Mit Nierenkomplikationen

E10.3 Mit Augenkomplikationen

E10.4 Mit neurologischen Komplikationen

E10.5 Mit peripheren vaskulären Komplikationen

E10.6 Mit sonstigen näher bezeichneten Komplikationen

E10.7 Mit multiplen Komplikationen

E10.8 Mit nicht näher bezeichneten Komplikationen

E10.9 Ohne Komplikationen

E10 – E14 Diabetes mellitus

IV Endokrine, Ernährungs- und Stoffwechselkrankheiten

Multiple

hierarchies

Single

hierarchies

Logic based

axioms

Preferred terms

and synonyms

Exclusion criteria

SNOMED CTontology- based terminology

ICD 10classification

� Preferred terms / rubrics:

� Terms are less ambiguous than clinical jargon

� Wording of terms do not include exclusion statements.

Example:

� E10 "Insulin-dependent diabetes mellitus" means

"Insulin-dependent diabetes mellitus which is not malnutrition-related,

neonatal, not in pregnancy, childbirth and the puerperium, not renal, not

an unspecified glycosuria, not and impaired glucose tolerance or a

postsurgical hypoinsulinaemia

� No term definitions, rather classification instructions

� Motivated by "classification principle" (disjoint classes)

ICD under a terminological view

Ingenerf J, Giere W. Concept-oriented standardization and statistics-oriented classification: continuing the classification versus nomenclature controversy.

Methods Inf Med. 1998 Nov;37(4-5):527-39.

� L21 Seborrhoeic dermatitis

� L21.0 Seborrhoea capitis

� L21.1 Seborrhoeic infantile dermatitis

� L21.8 Other seborrhoeic dermatitis

� L21.9 Seborrhoeic dermatitis, unspecified

� L21.8 equivalentTo L21 and

(not (L21.0 or L21.1 or L21.2))

� Caveat: residual categories are defined by their

siblings – lexical mapping misleading

Logic of residual categories

� Problems

� "Parts as parents" also in ICD

� icd10: Q21 Congenital malformations of cardiac septa

� icd10: Q21.3 Tetralogy of Fallot

� Heterogeneity of standard interpretation of rubrics

� icd11:HZ8 Chronic peripheral venous insufficiency

� icd11:HZ8.3 Lower limb varicose veins

� Currently discussed solution

� Diseases / disorders as Situations

� "A clinical situation with X is a phase of a patient's life in

which a condition of the type X is wholly present"

Ontological commitment

(which is the kind of things that are classified)

clinical disposition ?

clinical process?

clinical structure?

clinical structure?

Situation

Situation

Situation

Situation

isA

isA

isA

Ontological commitment

(which is the kind of things that are classified)

1. Classification Properties: Parents, Type, Use2. Textual Definition(s): Fully Specified Name3. Terms: synonyms, Index, inclusion, exclusion4. Clinical Description: Body System(s), Body

Part(s), [Anatomical Site(s), Histopathology5. Manifestation Properties: Signs & Symptoms,

Findings6. Causal Properties: etiology type, agents,

mechanisms, genomic characteristics; risk factors

7. Temporal Properties: age of occurrence & occurrence Frequency, development course

8. Severity Properties9. Functioning Properties10. Specific Condition Properties11. Treatment Properties12. Diagnostic Criteria13. External Causes

Linearizations

Morbidity

Primary Care

Mortality

SpecialtyAdaptation

ICD 11 Foundation Component and

Linearizations

ICD-11 content model parameters

Thanks: Nenad Kostanjsek, WHO

Principles of ICD-SNOMED mapping within

WHO IHTSDO Joint Advisory Group (JAG)

� Goal: common ontological basis for both the (polyhierarchical)

ICD-11 foundation component and SNOMED CT

� Ideally, each class in the ICD-11 foundation component

corresponds to exactly one class in SNOMED CT. Exceptions:

navigational classes, classes with exclusions

� The equivalence in meaning between these class pairs will be

assured by common text definitions.

� The transitive closure of taxonomic (subclassOf) relations in ICD-

11-FC is included in the transitive closure of subClassOf relations

in SNOMED CT.

Mapping principle

Edges correspond to subClassOf links. Each ICD class corresponds to exactly one SNOMED CT

class (same letter).

SubClassOf - links contained in ICD but not SNOMED can be obtained by transitive closure.

ICD 11 FC SNOMED CT

Ontology-based SNOMED CT - ICD11 mapping requires

that in both systems

1.The semantics of the subclass relation is shared

2.Classes to be mapped denote the same entities

3.The specific difference in architecture and meaning

between the two vocabularies are maintained

Exclusions in WHO terminologies

� Exclusions:

� Many ICD classes that carry (or inherit) exclusions

� Classes with exclusions are managed in the foundational

component

� Classes with exclusions do normally not exactly map to

SNOMED CT concepts

� e.g. icd:Acute pericarditis excludes Rheumatic pericarditis

� icd:AcutePericarditis equivalentTo

sct:AcutePericarditis and (not RheumaticPericarditis)

Linearizations, derived from Foundation component

SNOMEDCT

Common Ontology(definitions)

Foundation Component

shares ontological core

with SNOMED CT and

contains additional

non-ontological

knowledge (signs,

symptoms, causes,

linkage entities,

exclusion statement)

Common Ontologya subset of SNOMED CT classes and axioms

Mortality Morbidity Primary Care …

• Classification Properties: Parents, Type, Use

• Textual Definition(s): Fully Specified Name• Terms: synonyms, Index, inclusion,

exclusion• Clinical Description: Body System(s), Body

Part(s), [Anatomical Site(s), Histopathology• Manifestation Properties: Signs &

Symptoms, Findings• Causal Properties: etiology type, agents,

mechanisms, genomic characteristics; risk factors

• Temporal Properties: age of occurrence & occurrence Frequency, development course

• Severity Properties• Functioning Properties• Specific Condition Properties• Treatment Properties• Diagnostic Criteria• External Causes

MorbidityLinearization

Example:

“Diabetes Mellitus

excluding Pregnancy”

Links between Foundation Component

and Linearizations

SELECT ?CN WHERE

(?CN SubClassOf

‘Diabetes mellitus’)

MINUS

(?CN SubClassOf

Disorders of Pregnancy)

All linearization

entities are represented as

queries against the

Common Ontology

Conclusions

� (Classical) terminologies describe domain language,

ontologies describe domains

� Modern terminologies are increasingly ontology based

� Yet, terminologies tend to be hybrids that merge semantic,

epistemic and ontological features

� Logic-based formalisms and ontological commitment are

required for ontology-based terminology mapping

� SNOMED CT – vs. ICD: common interpretation of disease

codes as situations is preferred, ICD classes are best expressed

as queries on ontologies

� Terminologists should have an in-depth knowledge on

ontology

Literature

WWW

� Description Logics: http://dl.kr.org/

� Protégé: http://protege.stanford.edu/

� Bioontologies: http://www.bioontology.ch/

� Buffalo Ontology Site: http://ontology.buffalo.edu/smith/

� Bioportal: http://bioportal.bioontology.org/

� SNOMED CT: http://www.ihtsdo.org/snomed-ct/

http://terminology.vetmed.vt.edu/sct/menu.cfm

� ICD 10 http://apps.who.int/classifications/icd10

� ICD 11 http://apps.who.int/classifications/icd11/browse

Jorge Luis BorgesJorge Luis BorgesJorge Luis BorgesJorge Luis Borges

"On those remote pages "On those remote pages "On those remote pages "On those remote pages it is written that animals are it is written that animals are it is written that animals are it is written that animals are divided into:divided into:divided into:divided into:

a.a.a.a. those that belong to the those that belong to the those that belong to the those that belong to the Emperor Emperor Emperor Emperor

b.b.b.b. embalmed ones embalmed ones embalmed ones embalmed ones c.c.c.c. those that are trained those that are trained those that are trained those that are trained d.d.d.d. suckling pigssuckling pigssuckling pigssuckling pigse.e.e.e. mermaids mermaids mermaids mermaids f.f.f.f. fabulous ones fabulous ones fabulous ones fabulous ones

g.g.g.g. stray dogs stray dogs stray dogs stray dogs h.h.h.h. those that are included in those that are included in those that are included in those that are included in

this classificationthis classificationthis classificationthis classificationi.i.i.i. those that tremble as if they those that tremble as if they those that tremble as if they those that tremble as if they

were mad were mad were mad were mad j.j.j.j. innumerable ones innumerable ones innumerable ones innumerable ones k.k.k.k. those drawn with a very fine those drawn with a very fine those drawn with a very fine those drawn with a very fine

camel's hair brush camel's hair brush camel's hair brush camel's hair brush l.l.l.l. others others others others m.m.m.m. those that have just broken a those that have just broken a those that have just broken a those that have just broken a

flower vase flower vase flower vase flower vase n.n.n.n. those that resemble flies those that resemble flies those that resemble flies those that resemble flies

from a distance" from a distance" from a distance" from a distance"

The Celestial Emporium

of Benevolent Knowledge

Thank you!