Transcript Vortragstitel - Med Uni Graz

KR-MED 2008
Representing and Sharing Knowledge
Using SNOMED
Kent
Spackman
International Healthcare
Terminology Standards
Development Organisation
(IHTSDO)
SNOMED CT:
Ontological, Terminological, and
Knowledge Representation
Aspects
Stefan
Schulz
Medical Informatics
Research Group
University Medical Center
Freiburg, Germany
May 31, 2008, Phoenix, Arizona, USA
Purpose of the Tutorial (I)
 1: Theoretical underpinnings
 Get aware of the enormous variety of biomedical
vocabularies and their diverging architectural
principles
 Comprehend the current structure of SNOMED CT
as a result of its evolution
 Understand the nature of terminologies in contrast to
classifications, nomenclatures, and ontologies
 Understand the basic principles of formal ontology as
a foundation of modern vocabulary development
 Envisage the limitations of terminological /
ontological knowledge representation related to the
representation of domain knowledge in a broader
sense
Purpose of the Tutorial (II)
 2: The practice of SNOMED CT
 Understand the description logics used for
representing SNOMED CT
 Apply the description logics to special
requirements: partonomies, complex procedures
 Understand Pre-coordination and the SNOMED
CT compositional syntax
 Get insight into current redesign efforts (e.g.
substance redesign)
 Discuss the SNOMED CT context model and the
terminology / information model interface
Preliminary remarks
 Attendees:




Heterogeneous
Experts: please challenge our viewpoints
Novices: please ask if you don’t understand a term
All: participate actively, feel free to interrupt us
 We have enough time for (moderated)
discussions
 1st half: Presenter: Stefan, Moderator: Kent
 2nd half: Presenter: Kent, Moderator: Stefan
 Download tutorial slides from:
http://www.kr-med.org/2008/tutorial/tutorial1.zip
Biomedical Vocabularies
Understanding / Semantic Interoperability
data
Consumers
data
Health
Care
data
Enables
understanding
between human
and computational
agents
Biomedical
Research
Public
Health
data
Common languages: Biomedical Vocabularies
Meta – Terminological Issues
Biomedical
Vocabulary
Biomedical
Terminology
Biomedical
Ontology
SNOMED CT
Biomedical
Classification
- Real systems are not ideal
- Real systems are often hybrids
A cruise through the archipelago of biomedical
vocabularies
FBcv
MedDRA
MeSH
ChEBI
BRENDA
GO
MA
FMA
GENIA
TA
ICD
NCI
GALEN
SNOMED
WordNet
CLGRO
FAO
What biomedical vocabularies have in
common
Hierarchy
Node:
•
•
•
Code
Label
(Definition)
Semantics
B68
Taeniasis
B68.0
Taenia
solium
taeniasis
B68.1 Link
Taenia
saginata
taeniasis
B68.9
Taeniasis,
unspecified
MeSH: Medical Subject Headings
ICD
International Classification
of Diseases
CLASSIFICATION: ICD-10
CLASSIFICATION: ICD-10
CLASSIFICATION: ICD-10
Hierarchy of
Classes
Disjointness
(non-overlapping)
Exhaustiveness
CLASSIFICATION: ICD-10
CLASSIFICATION: ICD-10
 Nodes represent:
 Mutually disjoint classes of particular disease entities
 Often Idiosyncratic classification criteria
“I83 Varicose veins of lower extremities –
Excludes: complicating: pregnancy ( O22.0 ), puerperium ( O87.8 )”
 Classification criteria mix inherent properties of entities with
epistemic information
A15.1
Tuberculosis of lung, confirmed by culture only
 Labels: explanatory
 Terms: quasi-synonymous entry terms in different languages
(alphabetical index)
 Links:
 Connect classes with superclasses (taxonomy)
 Semantics:
 Taxonomy: All particular entities that instantiate one class, also
instantiate all superclasses
MeSH: Medical Subject Headings
MeSH
Medical Subject Headings
THESAURUS: Medical Subject Headings
THESAURUS: Medical Subject Headings
Hierarchical principle:
broader term / narrower
term
THESAURUS: Medical Subject Headings
THESAURUS: Medical Subject Headings
THESAURUS: Medical Subject Headings
 Nodes:
 Descriptors for content of biomedical publications
 Labels: Common, Unambiguous Terms; Definitions (scope notes)
 Terms: entry terms (synonyms, more specific terms) , translations
 Links:
 Polyhierarchical connections of “broader” with “narrower terms”
 Semantics:
Documents
Descriptor 1
Descriptor 2 is broader than descriptor 1
THESAURUS: Medical Subject Headings
 Nodes:
 Descriptors for content of biomedical publications
 Labels: Common, Unabbiguous Terms, Definitions (scope notes)
 Terms: entry terms (synonyms, more specific terms) , translations
 Links:
 Polyhierarchical connections of “broader” with “narrower terms”
 Semantics:
Descriptor 2
broader
Documents
narrower
Descriptor 1
Descriptor 2 is broader than descriptor 1
MeSH: Medical Subject Headings
TA
Terminologia Anatomica
NOMENCLATURE: Terminologia Anatomica
NOMENCLATURE: Terminologia Anatomica
 Nodes:
 Standardized Anatomical Terms (English / Latin)
“Junctura Membris Inferioris”- “Joints of Lower Limb”
 Links:
 Partonomic
 Semantics:
 A part of B: In an canonic instance of a human body the
anatomical structure denoted by A is included into the
anatomical structure denoted by B – and vice versa
MeSH: Medical Subject Headings
FMA
Foundational Model of
Anatomy
ONTOLOGY: Foundational Model of Anatomy
ONTOLOGY: Foundational Model of Anatomy
 Nodes:
 Classes of anatomical entities that constitute a canonic human
body
 Labels: Exact anatomical terms, compatible with TA
“Posterior ramus of third thoracic nerve”
 Terms: Synonyms and Translations
 Links:
 Taxonomic, Partonomic, Topological
 Semantics:
 Frame-based
 Taxonomy: All particular entities that instantiate one class, also
instantiate all superclasses
 A part-of B: In all canonic instances of a human body the
anatomical structure that instantiates A is included into the
anatomical structure that instantiates B
and vice versa
MeSH: Medical Subject Headings
GO
Gene Ontology
ONTOLOGY: Gene Ontology
ONTOLOGY: Gene Ontology
ONTOLOGY: Gene Ontology
Part of
(partonomy)
Is a
(taxonomy)
ONTOLOGY: Gene Ontology
 Nodes stand for:
 Originally: document/resource descriptors like MeSH, now:
classes of particular entities as delineated by the meaning of
the ontology labels
 Labels: unambiguous, self-explaining noun phrases
“low voltage-gated potassium channel auxiliary protein activity”
 Links:
 Connect classes with superclasses
 Connect parts with wholes
(taxonomy)
(partonomy)
 Semantics:
 Taxonomy: All particular entities that instantiate one class, also
instantiate all superclasses
 A part of B: All particular entities that instantiate A are part of at
least one particular entity that instantiates B
MeSH: Medical Subject Headings
openGALEN
ONTOLOGY: OpenGALEN
('SurgicalProcess' which
IsMainlyCharacterisedBy
{Performance
IsEnactmentOf ('SurgicalFixing' which
hasSpecificSubprocess ('SurgicalAccessing'
hasSurgicalOpenClosedness
(SurgicalOpenClosedness which hasAbsoluteState
surgicallyOpen))
actsSpeclflcallyOn (PathologlcalBodyStructure which <
Involves Bone
hasUniqueAssociatedProcess FracturingProcess
hasSpecificLocation (Collum whlch
IsSpecificSolidDivisionOf (Femur whlch
hasLeftRlghtSelectorleftSelect!on))>))))
ONTOLOGY: OpenGalen
 Nodes:
 Medical Concepts
 Labels: Artificial, Self-Explaining:
“SurgicalOpenClosedness”
 Links:
 Taxonomic, partonomic, other relations
 Semantics:




Description Logics T-Box (unary and binary predicates)
Non partonomic relations as existential restrictions
Sanctioning
Closed-world semantics
Better understanding SNOMED CT
MeSH: Medical Subject Headings
SNOMED CT
SNOMED since 1965
Fusion with
CTV 3
Principles of
Formal
Ontology
Context
Model
Logic-based
descriptions
multiaxial
nomenclature of
medicine
Nomenclature /
Pathology
1965
SNOP
1970
SNOMED
Embryo
1975
SNOMED
im UMLS
1980
SNOMED II
Fetus
1985
1990
IHTSDO
1995
2000
2005
SNOMED 3.0 SNOMED 3.5 SNOMED RT SNOMED CT
Infant
Child
Adolescence
SNOMED CT
 The current structure of SNOMED CT is a result
of its evolution
 Represents several tendencies from decades of
nomenclature, terminology, ontology, and
classification system development
Formal Language
Nomenclature:
Multiaxial Structure
Benign neoplasm of heart =
Thesaurus
64572001|disease|:
{116676008|hasasociated morphology|
=3898006|neoplasm, benign|
,363698007|finding site|=80891009|heart
structure|}
SNOMED CT
Ontological
Principles
Sanctioning
Clinically relevant
classes
SNOMED CT
 The current structure of SNOMED CT is a result
of its evolution
 Represents several tendencies from decades of
nomenclature, terminology, ontology, and
classification system development
 Identification of elements of
 Terminology
 Ontology
SNOMED CT
 The current structure of SNOMED CT is a result
of its evolution
 Represents several tendencies from decades of
nomenclature, terminology, ontology, and
classification system development
Terminology vs. Ontology
What biomedical vocabularies have in
common
Hierarchy
Node:
•
•
•
Code
Label
(Definition)
Semantics
B68
Taeniasis
B68.0
Taenia
solium
taeniasis
B68.1 Link
Taenia
saginata
taeniasis
B68.9
Taeniasis,
unspecified
Terminology vs. Ontology
Dictionaries of
Natural language Terms
Hierarchically ordered
Nodes and Links
Formal or informal
Definitions
bla bla bla
• 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
D18
Benign
Neoplasm
…
Terminology
Heart Neoplasms [MeSH]:
Tumors in any part of the heart.
They include primary cardiac
tumors and metastatic tumors to
the heart. Their interference with
normal cardiac functions can
cause a wide variety of symptoms
Formal Ontology
D18.0
Benign
Neoplasm
of
Thymus
Set of terms representing
the system of concepts of
a particular subject field.
(ISO 1087)
D18.1
Benign
Neoplasm
of
Heart
Benign neoplasm of heart
(disorder)
B68.9
[SNOMED CT]:
Taeniasis,
64572001|disease|:
unspecified
{116676008|associated morphology|
=3898006|neoplasm, benign|
,363698007|finding site|=80891009|heart
structure|}
Ontology is the study of what there is.
Formal ontologies are theories that
attempt to give precise mathematical
formulations of the properties and
relations of certain entities.
(Stanford Encyclopedia of Philosophy)
Terminology
Dictionaries of
Natural language Terms
Hierarchically ordered
Nodes and Links
Formal or informal
Definitions
bla bla bla
• 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
D18.0
• Tumeur bénigne du cœur
Benign
Neoplasm
• Neoplasia cardíaca benigna
of
• Neoplasia benigna do coração
Thymus
• Neoplasia benigna
del corazón
„benign
neoplasm of heart“
• Tumor benigno do corazón
Entities of
Language
(Terms)
Heart Neoplasms [MeSH]:
Tumors in any part of the heart.
They include primary cardiac
tumors and metastatic tumors to
the heart. Their interference with
normal cardiac functions can
Shared /
cause a wide variety of symptoms
D18
Benign
Neoplasm
…
D18.1
Benign
Neoplasm
of
Heart
Meanings /
Entities of
Benign neoplasm
of heart
Thought
(disorder)
(Concepts)
B68.9
[SNOMED CT]:
Taeniasis,
64572001|disease|:
unspecified
{116676008|associated morphology|
=3898006|neoplasm, benign|
,363698007|finding site|=80891009|heart
structure|}
„gutartige Neubildung des Herzmuskels”
“neoplasia cardíaca benigna”
Example: UMLS (mrconso table)
Shared /
Meanings /
Entities of
Thought
Entities of
Language
(Terms)
C0153957|ENG|P|L0180790|PF|S1084242|Y|A1141630||||MTH|PN|U001287|benign neoplasm of heart|0|N||
C0153957|ENG|P|L0180790|VC|S0245316|N|A0270815||||ICD9CM|PT| 212.7|Benign neoplasm of heart|0|N||
C0153957|ENG|P|L0180790|VC|S0245316|N|A0270817||||RCD|SY|B727.| Benign neoplasm of heart|3|N||
C0153957|ENG|P|L0180790|VO|S1446737|Y|A1406658||||SNMI|PT|
D3-F0100|Benign neoplasm of heart, NOS|3|N||
C0153957|ENG|S|L0524277|PF|S0599118|N|A0654589||||RCDAE|PT|B727.|Benign tumor of heart|3|N||
C0153957|ENG|S|L0524277|VO|S0599510|N|A0654975||||RCD|PT|B727.| Benign tumour of heart|3|N||
C0153957|ENG|S|L0018787|PF|S0047194|Y|A0066366||||ICD10|PS|D15.1|Heart|3|Y||
C0153957|ENG|S|L0018787|VO|S0900815|Y|A0957792||||MTH|MM|U003158|Heart <3>|0|Y||
C0153957|ENG|S|L1371329|PF|S1624801|N|A1583056|||10004245|MDR|LT|10004245|Benign cardiac neoplasm|3|N||
C0153957|GER|P|L1258174|PF|S1500120|Y|A1450314||||DMDICD10|PT| D15.1|Gutartige Neubildung: Herz|1|N||
C0153957|SPA|P|L2354284|PF|S2790139|N|A2809706||||MDRSPA|LT| 10004245|Neoplasia cardiaca benigna|3|N||
Unified Medical Language System, Bethesda, MD: National Library of Medicine, 2007:
http://umlsinfo.nlm.nih.gov/
Example: UMLS (mrrel table)
Shared /
Meanings /
Entities of
Thought
Shared /
Meanings /
Entities of
Thought
C0153957|A0066366|AUI|PAR|C0348423|A0876682|AUI |
|R06101405||ICD10|ICD10|||N||
C0153957|A0066366|AUI|RQ |C0153957|A0270815|AUI |default_mapped_ from|R03575929||NCISEER|NCISEER|||N||
C0153957|A0066366|AUI|SY |C0153957|A0270815|AUI |uniquely_mapped_ to |R03581228||NCISEER|NCISEER|||N||
C0153957|A0270815|AUI|RQ |C0810249|A1739601|AUI |classifies
| R00860638||CCS|CCS|||N||
C0153957|A0270815|AUI|SIB|C0347243|A0654158|AUI |
|R06390094
|| ICD9CM|ICD9CM||N|N||
C0153957|A0270815|CODE|RN|C0685118|A3807697|SCUI |mapped_to
| R15864842||SNOMEDCT|SNOMEDCT||Y|N||
C0153957|A1406658|AUI|RL |C0153957|A0270815|AUI |mapped_from
| R04145423||SNMI|SNMI|||N||
C0153957|A1406658|AUI|RO |C0018787|A0357988|AUI |location_of
| R04309461||SNMI|SNMI|||N||
C0153957|A2891769|SCUI|CHD|C0151241|A2890143|SCUI|isa
|R19841220|47189027|SNOMEDCT|SNOMEDCT|0|Y|N||
Semantic relations
Example: UMLS
Shared /
Meanings /
Entities of
Thought
Shared /
Meanings /
Entities of
Thought
C0153957|A0066366|AUI|PAR|C0348423|A0876682|AUI |
|R06101405||ICD10|ICD10|||N||
C0153957|A0066366|AUI|RQ |C0153957|A0270815|AUI |default_mapped_ from|R03575929||NCISEER|NCISEER|||N||
C0153957|A0066366|AUI|SY |C0153957|A0270815|AUI |uniquely_mapped_ to |R03581228||NCISEER|NCISEER|||N||
C0153957|A0270815|AUI|RQ |C0810249|A1739601|AUI |classifies
| R00860638||CCS|CCS|||N||
C0153957|A0270815|AUI|SIB|C0347243|A0654158|AUI |
|R06390094
|| ICD9CM|ICD9CM||N|N||
C0153957|A0270815|CODE|RN|C0685118|A3807697|SCUI |mapped_to
| R15864842||SNOMEDCT|SNOMEDCT||Y|N||
C0153957|A1406658|AUI|RL |C0153957|A0270815|AUI |mapped_from
| R04145423||SNMI|SNMI|||N||
C0153957|A1406658|AUI|RO |C0018787|A0357988|AUI |location_of
| R04309461||SNMI|SNMI|||N||
C0153957|A2891769|SCUI|CHD|C0151241|A2890143|SCUI|isa
|R19841220|47189027|SNOMEDCT|SNOMEDCT|0|Y|N||
INFORMAL
Semantic relations
Formal Ontology represents the world
bla bla bla
Terminology
Set of terms
representing the system
of concepts of a
particular subject field.
(ISO 1087)
Formal
Ontology
Ontology is the study of what
there is (Quine).
Formal ontologies are theories
that attempt to give precise
mathematical formulations of the
properties and relations of
certain entities.
(Stanford Encyclopedia of Philosophy)
Formal Ontology
Organizing Entities
Entity Types
The type
“benign
neoplasm of
heart”
My benign
neoplasm of
heart
Entities of
the World
Organizing Entities
abstract
Entity Types
The type
“benign
neoplasm of
heart”
Universals, classes,
(Concepts)
Instance_of
concrete
Entities of
the World
Particulars,
instances
The benign
neoplasm of my
heart
Organizing Entities
abstract
represents
Entity Types
The type
“benign
neoplasm of
heart”
Universals, classes,
(Concepts)
Entities of
Language
Instance_of
Terms, names
represents
concrete
The string
„benign neoplasm of heart“
Entities of
the World
Particulars,
instances
The benign
neoplasm of my
heart
Organizing Entities
(the complication
of my)
benign heart tumor
(die
Komplikation
meines)
Gutartigen
Herztumors
represents
Organizing Entities
represents
(the)
benign heart tumor
(is congenital)
Terms, names
(die
Komplikation
meines)
Gutartigen
Herztumors
Entities of
Language
…are represented by
terminologies
Databases systems represent …
Entities of
the World
Entity Types
… are organized in formal ontologies
Hierarchies, Types, Classes, Individuals
World
Hierarchies, Types, Classes, Individuals
World
Hierarchies, Types, Classes, Individuals
Type 1
World
Hierarchies, Types, Classes, Individuals
Formal Ontology
Is_a
Subtype
1.1
World
Type 1
Is_a
Subtype
1.2
Is_a
Subtype
1.3
Hierarchies, Types, Classes, Individuals
Formal Ontology
Inflammatory
Disease
Hierarchies, Types, Classes, Individuals
Formal Ontology
Inflammatory
Disease
Is_a
Is_a
Gastritis
Hepatitis
Is_a
Pacreatitis
Hierarchies, Types, Classes, Individuals
Formal Ontology
Inflammatory
Disease
Is_a
Is_a
Gastritis
Hepatitis
Is_a
Pacreatitis
Hierarchies, Types, Classes, Individuals
Formal Ontology
Inflammatory
Disease
Is_a
Is_a
Gastritis
Hepatitis
Is_a
Pacreatitis
Relations and Definitions
Formal Ontology
Inflammatory
Disease
Is_a
Hepatitis
has
Location
Liver
Relations and Definitions
Formal Ontology
Inflammatory
Disease
Is_a
Hepatitis
has
Location
Liver
Relations and Definitions
Formal Ontology
Inflammatory
Disease
Is_a
Population
Is_a
Population of Virus
Hepatitis
caused
by
Viral Hepatitis
has
Location
Liver
Languages for formal ontologies
 Natural Language
“Every hepatitis is an inflammatory disease that is located in some liver”
“Every inflammatory disease that is located in some liver is an hepatitis”
 Logic
x: instanceOf(x, Hepatitis)  instanceOf(x, Inflammation) 
y: instanceOf(y, Liver)  hasLocation(x,y)
Logic is computable: it supports
machine inferences but…
it only scales up if it has a very
limited expressivity
SNOMED CT: Terminology and Ontology
aspects
bla bla bla
Fully Specified Name
Preferred Term
Taxonomic
Parents (isA)
Synonyms
Same structure for other languages
Terminology
Logical
Restrictions
Full-text definitions mostly missing
Formal Ontology
Terminologies vs. Formal Ontologies
Terminologies
 Describe: Meaning of human
language units
 “Concepts”: aggregate (quasi)synonymous terms
 Relations: informal, elastic
Associations between Concepts
……..
 Description pattern:
Concept1 Relation Concept2
Formal Ontologies
 Describe: entities of reality as
they generically are –
independent of human
language
 “Types”: represent the generic
properties of world entities
 Relations: rigid, exactly
defined, quantified
relationships between
particulars
 Description pattern:
for all instance of Type1 : there
is some…
Example Hepatitis - Liver
Terminologies
 Concept Hepatitis:
{Hepatitis (D), Leberentzündung (D),
hepatitis (E), hépatite (F)}
 Concept Liver:
{Leber (D), liver (E), foie (F)}
 Relations:
 Hepatitis – hasLocation – Liver
 Hepatitis – isA - Inflammation
Formal Ontologies
 Type: Hepatitis:
 Description:
”Every hepatitis is an inflammatory
disease that is located in some liver”
“Every inflammatory disease that is
located in some liver is an hepatitis”
Example Hand - Thumb
Terminologies
 Concept Hand:
Formal Ontologies
 Type: Thumb:
{Hand (D), hand (E), main (F)}
 Concept Thumb:
 Description:
{Daumen (D), thumb (E), pouce (F)}
 Relations:
 Hand – hasPart – Thumb
 Thumb – partOf – Hand
”Every thumb is part of some hand”
“Every hand has some thumb as
part”
?
Example Aspirin - Headache
Terminologies
 Concept Aspirin:
{Aspirin (D,E), Acetylsalicylsäure (D),
ASS (D), acetylsalicylic acid (E), Acide
acétylsalicylique(F)}
 Concept Headache:
{Kopfschmerz (D), headache (E),
céphalée(F)}
 Relation:
Formal Ontologies
 Type: Aspirin:
 Description:

”For every portion of aspirin there is
some disposition for treating
headache”
 Aspirin – treats – Headache
fuzzy
complicated !
Strengths of Formal Ontologies
 Exact, logic-based descriptions of entity types that
are instantiated by real-world objects, processes,
states
 Representation of stable, context-independent
accounts of reality
 Use of formal reasoning methods using tools and
approaches from the AI / Semantic Web
community
Formal Ontologies: Limitations (I)
 Only suitable to represent shared, uncontroversial
meaning of a domain vocabulary
 Supports universal statements about instances of
a type:
 All Xs are Ys
 For all Xs there is some Y
 Properties of types are properties of all entities that
instantiate these types (strict inheritance)
Classification vs. Ontology
Classification systems vs. Ontologies
Classifications vs. Formal Ontologies
Classifications
Formal Ontologies
A
A
A1
A2
A3
A4
A
nec
A
nos
“not
elsewhere
classified”
“not
otherwise
specified”
A1
A2
A3
A4
Classifications vs. Formal Ontologies
Classifications
Diabetes
Mellitus
Diabetes
Mellitus
Formal Ontologies
Diabetes
Mellitus
Diabetes
Mellitus
In Pregnancy
SNOMED CT: Classification aspects
SNOMED CT and Classifications
 Many classes in classification systems cannot be
adequately expressed in SNOMED
 Problem:
 SNOMED supports existential quantification and
conjunction, but not negation
 Classifications contain classes defined by negation:
Viral hepatitis (B15-B19)
Excludes: cytomegaloviral hepatitis ( B25.1 )
herpesviral [herpes simplex] hepatitis ( B00.8 )
sequelae of viral hepatitis ( B94.2 )
B17 Other acute viral hepatitis
B17.0 Acute delta-(super)infection of hepatitis B carrier
B17.1 Acute hepatitis C
B17.2 Acute hepatitis E
B17.8 Other specified acute viral hepatitis Hepatitis non-A non-B (acute)(viral) NEC
Knowledge Representation
Continuum of knowledge
Universally accepted
assertions
Consolidated but contextdependent facts
Hypotheses, beliefs,
statistical associations
Domain Knowledge
Formal Ontology !
Universally accepted
assertions
Consolidated but contextdependent facts
Hypotheses, beliefs,
statistical associations
Domain Knowledge
Instance-level Knowledge / Belief
 Working Hypothesis
The patient was admitted with suspected appendicitis
 Unknown facts
Allergies unknown
 Ruled-out facts
No Pregnancy
Absent corneal reflex
 Imprecise
Patient reports “liver disease”
 Epistemic
The diabetes was recently diagnosed
 Classification-related:
Cause of death: A09 - Diarrhoea and gastroenteritis of presumed infectious
origin
Diagnosis: B37.8 - Candidiasis of other sites
Domain Knowledge
 Facts that are known to be true under certain circumstances:
Excessive alcohol consumption can cause gout
 Context dependent facts:
Hg2Cl2 is a diuretic drug
Aspririn is an analgetic drug
 Facts about populations:
Malaria is endemic in Mozambique
 Recommendations / Guidelines:
Old patients with newly diagnosed Hypertension should be treated with
diuretics or Ca channel blockers
 Basic scientific facts
Many urokinase-type plasminogen activators are expressed in the kidney
 Results from clinical trials:
One-lung overventilation does not induce inflammation in the normally
ventilated contralateral lung.
 Default / canonic knowledge
„Adult humans have 32 teeth“
Take home messages
 Ontologies describe classes of domain entities
(ideally) by their inherent properties
 Classifications classify entities according to welldefined criteria
 Terminologies relate words and terms
 SNOMED CT is a hybrid terminology / ontology
with elements of classifications
 Knowledge representation extends terminology /
ontology by large
 (Computable) Ontologies are restricted to make
universal statements of the type for all… some
Practice of Good Ontology
Practice of Good Ontology
Learning good ontology practice from bad
ontologies…
Don’t mix up universals (Concepts, Classes) with
individuals (Instances)
Is_a = subclass_of:
 subclass-of (Motor Neuron, Neuron)
(FMA, OpenGALEN)
 Is_a (Motor Neuron, Neuron)
 instance-of (Motor Neuron, Neuron) (FlyBase)
But:
 instance-of (my Hand, Hand)
 instance-of (this amount of insulin, Insulin)
 instance-of (Germany, Country)
 not: instance of (Heart, Organ)
 not: instance of (Insulin, Protein)
Taxonomic
Subsumption
Instance_of
Class Membership
Don’t use superclasses to express roles
 Is_a (Fish, Animal)
 Is_a (Fish, Food) ??
 Is_a (Acetylsalicylic Acid, Salicylate)
 Is_a (Acetylsalicylic Acid, Analgetic Drug) ??
Be aware of the “rigidity” of entity types
Partition the ontology by principled upper level
categories
Example: DOLCE’s Upper Ontology
Endurant (Continuant)
Physical
Amount of matter
Physical object
Feature
Non-Physical
Mental object
Social object
…
Perdurant (Occurrent)
Static
State
Process
Dynamic
Achievement
Accomplishment
Quality
Physical Qualities
Spatial location
…
Temporal Qualities
Temporal location
…
Abstract Qualities
…
Abstract
Quality region
Time region
Space region
Color region
Source: S. Borgo ISTC-CNR
Limit to a parsimonious set of semantically
precise Basic Relations
Barry Smith, Werner Ceusters, Bert Klagges, Jacob Köhler,
Anand Kumar, Jane Lomax, Chris Mungall, Fabian Neuhaus,
Alan L Rector and Cornelius Rosse. Relations in biomedical ontologies.
Genome Biology, 6(5), 2005.
Avoid idiosyncratic categorization
Body structure (10)
Acquired body structure
Anatomical organizational pattern
(…)
Clinical finding (22)
Administrative statuses
Adverse incident outcome categories
(…)
Environment or geographical location
Environment
Geogr. and/or political region of the world
Event (19)
Abuse
Accidental event
Bioterrorism related event
(…)
Linkage concept
Attribute
Link assertion
Observable entity
Age AND/OR growth period
Body product observable
(…)
Clin. history / examination observable (21)
Device observable
Drug therapy observable
Feature of Entity
(…)
Organism (11)
Animal
Chromista
Infectious agent
(…)
Pharmaceutical / biologic product (58)
Alcohol products
Alopecia preparation
Alternative medicines
(…)
Physical force (21)
Altitude
Electricity
(…)
Physical object (8)
Device
Domestic, office and garden
artefact
Fastening
(…)
Procedure (23)
Administrative procedure
Community health procedure
(…)
Qualifier value (52)
Action
Additional dosage instructions
(…)
Record artifact
Record organizer
Record type
Situation with explicit
context (17)
A/N risk factors
Critical incident factors
(…)
Social context (10)
Community
Family
Group
(…)
Special concept
Namespace concept
Navigational concept
Non-current concept
Specimen (45)
Biopsy sample
Body substance sample
Cardiovascular sample
(…)
Staging and scales (6)
Assessment scales
Endometriosis classification of
American Fertility Society
(…)
Substance (11)
Allergen class
Biological substance
The Celestial Emporium
of Benevolent Knowledge
Jorge Luis Borges
"On those remote pages
it is written that animals
are divided into:
a. those that belong to the
Emperor
b. embalmed ones
c. those that are trained
d. suckling pigs
e. mermaids
f. fabulous ones
g. stray dogs
h. those that are included
in this classification
i. those that tremble as if
they were mad
j. innumerable ones
k. those drawn with a
very fine camel's hair
brush
l. others
m. those that have just
broken a flower vase
n. those that resemble
flies from a distance"
Be aware of ambiguities

“Institution” may refer to
1. (abstract) institutional rules
2. (concrete) things instituted
3. act of instituting sth.

“Tumor”
1. evolution of a tumor as a disease process
2. having a tumor as a pathological state
3. tumor as a physical object

“Gene”
1. a (physical) sequence of nucleotides on a DNA
chain
2. a collection of (1)
3. A piece of information conveyed by (1)
Don‘t mix up ontology with epistemiology
 Is_a (Infection of unknown origin, Infection)
 Is_a (Newly diagnosed diabetes, Diabetes)
 Is_a (Family history of diabetes, Diabetes)
„what is“
„what sth.
knows about “
Don‘t mix up Ontology IDs with Terms
• Glycerin Kinase
• Glycerokinase
• GK
•Glyzerinkinase
„how it is expressed
in human language“
„what is“
„what sth.
knows about “
Don’t underestimate Ontology Maintenance
 Formal Ontologies must always be maintained
 consistent (free of logic contradiction): prerequisite for
machine reasoning
 adequate (correctly describe the domain) prerequisite to
prevent erroneous deductions
 Maintenance load is much higher than with
terminologies.
 Ontology maintenance is mainly task of domain
experts. IT staff has supportive function
 Typical design and maintenance errors
Aspects of Knowledge Representation
 Terminological Knowledge: What is known about
the meaning of terms in a domain
“neoplasm” has a broader meaning as “sarcoma”
 Ontological “Knowledge”: What is univocally
accepted as generic properties of types of entities
of a domain (often definitional or trivial):
every hepatitis is located in some liver
every cell has some cell membrane
 Terminologies and Ontologies represent this kind
of Knowledge, but…
 Knowledge representation is more:
Knowledge Representation
in Practice: SNOMED CT
Kent A. Spackman, MD, PhD
Chief Terminologist, IHTSDO
Clinical Professor, Oregon Health & Science University, Portland Oregon USA
KR-MED 2008 Tutorial
Phoenix – May 30, 2008
Tutorial 2nd Half Topics
• Description logic basics
• Concept model domains, ranges and attributes
• Application of additional description logic features to
special requirements:
–
–
–
Right identities: partonomies (SEP)
Role groups: complex procedures, disorders, situations
Role hierarchies: direct and indirect objects; causal and associational relationships
• Composition and syntax
– normal forms
– SNOMED compositional syntax, KRSS, and OWL
• Current redesign efforts
– substances, observables, anatomy, events, conditions, organisms
• The context model & negation
• The terminology / information model interface
DESCRIPTION LOGIC
BASICS
What is description logic?
• Mathematical viewpoint:
– A family of logics characterized by
• Formal set-theoretic semantics
• Proofs of correctness and completeness of computation
• Proofs of algorithmic complexity (PSpace, NP-complete,
NExpTime, etc)
• Knowledge representation viewpoint:
– A set of constructs for representing terminological
knowledge (that which is always true of a meaning)
– Algorithms and their implementations for performing:
• Subsumption (testing pairs of expressions to see
whether one is a subtype of the other & vice versa)
• Classification (structuring a set of expressions according
to their subsumption relationships)
Constructs for a very simple DL: EL
Name of construct
Primitive concept name
Notation Semantics
A
AI µ ¢ I
Primitive role name
R
RI µ ¢ I £ ¢ I
Top (everything)
>
¢I
Conjunction (AND)
CuD
CI \ D I
Exists restriction
9R:C
f xj9y.R I (x;y)^CI (y)g
Concept definition
(sufficient)
A´ C
AI =CI
Primitive concept
introduction
Av C
AI µ CI
Reading DL expressions
u
is the symbol for Boolean conjunction
– Short name: “and”
– Operands are placed on either side: C u D
– Both operands C and D must be true in order for the expression to be
true
9
is the symbol for existential restriction
– Short name: “some”
– Operands follow the backwards E symbol: 9R¢C
• R represents a “role” or relationship
• C represents the value or target of the relationship
– Requires that there be an instance of relationship named R to some
instance of the class named C in order for the expression to be true
The logic of green frogs
How could you use EL to define a green frog?
First, some concept names (A in the EL notation table):
Frog
Green
GreenFrog
Then a role name (R in the EL notation table):
hasColor
Now introduce a definition of GreenFrog,
using Frog, Green, and hasColor, along with C u D and 9R¢C as follows:
GreenFrog ´ Frog u 9 hasColor.Green
The logic of green frogs
How do we read these expressions?
Usual DL syntax
GreenFrog ´ Frog u 9 hasColor.Green
KRSS syntax:
(defconcept GreenFrog (and Frog (some hasColor Green)))
Every instance of the class named GreenFrog
is an instance of the class named Frog which also
has a “hasColor” relationship to an instance
of the class named “Green”
And, every instance of the class named Frog which also
has a “hasColor” relationship to an instance
of the class named “Green”
is an instance of the class named GreenFrog.
The logic of green frogs
Right hand side only:
Usual DL syntax
Frog u 9 hasColor.Green
KRSS syntax:
(and Frog (some hasColor Green))
SNOMED compositional syntax:
Frog: hasColor = Green
an instance of the class named Frog which also
has a “hasColor” relationship to an instance of the
class named “Green”
Exercise for the reader: how do you represent frogs that are
completely green?
A SNOMED example
• Headache is-a ache: finding-site = head structure
– (and headache is marked as “defined” in concepts table).
• The class “headache” is sufficiently defined as the
set of instances of the class “ache” which also have
at least one finding-site relationship to an instance
of the class “head structure”.
• And all instances of class “ache” with some findingsite relationship to an instance of “head structure”
are instances of “headache”.
• Now, is that what you mean when you say “headache”?
EXPRESSIONS &
COMPOSITION
SNOMED CT Expressions
•
SNOMED CT coded information consists of structured (composed)
collections of concept codes
– These are called expressions
– The meaning of an expression depends on the situation in which it is used
Example
•
The SNOMED CT code for “fracture of femur” represents the meaning
of “a break in a femur”
– Depending on where it is used in a patient record, the code may mean
•
•
•
•
The patient has a fractured femur
The patient’s main diagnosis is a fracture of the femur
The patient has a past history of fractured femur
The patient is suspected of having a fractured femur …. etc
– In a query it may be one of several criteria for retrieving the records of
patients with particular types of injury
– In an index to the clinical literature it might indicate a paper that is
relevant to this condition
Expressions can be pre-coordinated or post-coordinated
• Pre-coordinated expression
– Terminology producer provides a single ConceptId for the
meaning
• 31978002
– means “fracture of tibia”
• Post-coordinated expression
– A user composes a combination of ConceptIds to represent
the meaning
• 31978002 : 272741003 = 7771000
– (fracture of tibia : laterality = left)
– In human readable form … “fracture of left tibia”
Refinement and qualification:
Two common ways to derive post-coordinated expressions
• Refinement
– Replacing value C with a more specific value C1 within an
existing (defining) 9R¢C relationship in the definition,
giving 9R¢C1
– Example
• Fracture of femur
– Defined as: finding-site = bone structure of femur
– May be refined to: finding-site = structure of neck of femur
• Yielding the new meaning: Fracture of neck of femur
Refinement and qualification:
Two common ways to derive post-coordinated expressions
• Qualification (also called “subtype qualification”)
– Replacing value C with a more specific value C1 within a
qualifier 9R¢C relationship (found in the qualifying
relationships in the relationships table), giving 9R¢C1
– Example
• Bronchitis
– Qualifier exists as: clinical-course = courses (any course value)
– May be qualified to: clinical-course = acute (sudden onset
AND/OR short duration)
• Yields the meaning: Acute bronchitis
• End results of refinement or qualification are post-coordinated
expressions with an identical logical structure
Compositional grammar (1)
• Simplest expression is a single conceptid
– For example
• 71620000
• Optionally conceptId may be followed by a term
enclosed in pipe delimiters
– For example
• 71620000|fracture of femur|
• Concepts can be combined with a plus sign that
means logical “and” (conjunction)
– For example
• 31978002|fracture of tibia| +75591007|fracture of fibula|
Compositional grammar (2)
• Refinements can be added after a colon
For example
125605004: 363698007=29627003
• Refinements can be nested in parentheses
For example
53057004|hand pain|:
363698007|finding site| =(76505004|thumb structure|:
272741003|laterality| =7771000|left|)
• Refinements can be grouped in braces
For example
71388002|procedure|:
{260686004|method| =129304002|excision - action|,
363704007|procedure site| =66754008|appendix structure|}
Note: the comma also means logical “and” in this expression
Severe pain, left thumb
Pain
Finding site
Thumb structure
Laterality
Severity
Severe
22253000|pain|:
363698007|finding site|=
(76505004|thumb structure|:
272741003|laterality|=7771000|left|),
246112005|severity|=24484000|severe|
Left
Severe pain, left thumb
Hand Pain
Finding site
Thumb structure
Laterality
Severity
Severe
53057004|hand pain|:
363698007|finding site|=
(76505004|thumb structure|:
272741003|laterality|=7771000|left|),
246112005|severity|=24484000|severe|
Left
Subtype relationships
• Every concept is a refined type of one or
more other concepts
• For example
– “Pain in the leg” is a type of “pain”
– “Pain in the leg” is a type of “lower limb
finding”
• SNOMED CT represents these defining
relationships with the relationship type “is
a”
Subtype relationships
Lower limb finding
Pain
Pain in lower limb
Pain in calf
A pain in the calf is-a pain the lower limb, and
Pain in the lower limb is-a pain, and is-a lower limb finding
Subtype relationships
Lung disease
Infectious disease
Infectious pneumonia
Bacterial pneumonia
Bacterial pneumonia is-a infectious pneumonia, and
Infectious pneumonia is-a lung disease, and is-a Infectious disease
Why have subtype relationships?
• Because when you selectively retrieve
information you usually want to include
subtypes
• For example
– When searching for “Deep Venous Thrombosis”
you would usually want to retrieve all kinds of
DVT including …
• DVT of specific sites (e.g. lower limb)
• DVT with particular causes (e.g. air travel related DVT)
… and others
Root
Subtype hierarchy
Clinical
finding
Looking from leaf to root
Disorder
Finding by site
Disorder by
body site
Finding of body
region
Disorder of
body system
Finding of limb
structure
Disorder of
cardiovascular system
Disorder of
extremity
Vascular
disease
Thrombotic
disorder
Disease of vein
Finding of lower
limb
Disorder of
lower extremity
Peripheral vascular
disease
Venous
thrombosis
Vascular disorder
of lower extremity
Deep venous
thrombosis
Thrombosis of vein
of lower limb
Deep venous thrombosis
of lower extremity
Leaf
Deep vein thrombosis of
leg related to air travel
DVT leg assoc w air travel
Other defining relationships
• The difference between two concepts may
be represented by other defining
relationships
– Only relationships that are necessarily
true are defining relationships
• For example
– “Pain in calf” has “finding site” “calf
structure”
Other defining relationships
Lower limb finding
Pain
Pain in lower limb
lower limb
structure
Calf structure
Pain in calf
A pain in the calf has finding site calf
Pain in the lower limb has finding site lower limb
Other defining relationships
Bacterial disease
Lung disease
Bacterial
pneumonia
Lung structure
RLL structure
RLL bacterial
pneumonia
Bacterial pneumonia has finding site lung structure
RLL bacterial pneumonia has finding site RLL structure
Why have other defining relationships?
• Other defining relationships
– Confirm and enhance the accuracy of the subtype
hierarchy
• For example, all “pain” findings with a “finding site” of
“lower limb” (or a subtype of lower limb structure) must
be subtypes of “lower limb pain”
– Enable concepts to be refined by increasing the
specificity of a defined relationship
• For example, a “pain in the foot” could be refined to
specify a more precise “finding site” such as the “third
toe of the left foot”, even if SNOMED CT did not include
a specific concept for pain in a such a specific location.
– Allow recognition of equivalence between different
ways of expressing the same concept
Primitive & sufficiently-defined concepts
• A concept is “sufficiently defined”
– if its definition is sufficient to
distinguish it from all its supertype
concepts
• A concept is “primitive”
– if it is not “sufficiently defined”
Primitive & sufficiently defined concepts
• Head injury
–
–
–
–
Is a = Disease
Associated morphology = Traumatic abnormality
Finding site = Head structure
Sufficiently Defined
• Aching pain
– Is a = Pain
– Primitive
• Headache
– Is a = Aching pain
– Finding site = Head structure
– Sufficiently Defined
The value of sufficiently defining concepts
• Allows auto-classification
– Consistent hierarchy and definition
• Allows computation of equivalence and
subsumption between
– Different ways of expressing the same meaning
• E.g.
 “open fracture of left femur”
or
– “fracture of bone”
 site=“femur”: laterality=“left”
 morphology=“open fracture”
Different views of relationships
• Stated view
– The view that SNOMED CT modelers edit
– Includes only the defining relationships that an
author has explicitly stated to be true
– (soon will be distributed in KRSS and/or OWL
syntax)
• Inferred view
– The view distributed in the distribution file
– Generated by auto-classification
– Includes relationships inferred from the stated view
– Excludes redundant relationships
• Normalized view
– The view best suited to comparing expressions
– Reduces all values to their proximal primitive
subtypes
Auto-classification
• Many relationships are inferred by autoclassification rather than authored directly
• Auto-classification
– Takes definitions “stated” by SNOMED authors and uses
them to “infer” other relationships
– Removes redundant (less specific) defining relationships
– Creates a logically consistent parsimonious set of
relationships
• Review the results of classification
– Although logically consistent … it may not be “correct”
due to errors in “stated” definitions
– Human errors that might otherwise be overlooked are
often highlighted by auto-classification
– Auto-classification is repeated frequently during authoring
and the results are then rechecked
An example of a stated view
The diagram is a “directed acyclic graph”, or DAG, of the is-a relationships
pain
is a
is a
pain in lower limb
lower limb structure
is a
pain in calf
calf structure
Auto-classification can add
inferred relationships to the stated view
pain
is a
pain in lower limb
is a
Inferred
from other
relationships
lower limb structure
is a
is a
calf structure
pain in calf
For the distributed inferred view less specific subtype
relationships are removed
In computer science terms this structure is called the “transitive reduction”
i.e. the distributed is-a relationships are the transitive reduction of the DAG
pain
Redundant after
adding inferred
relationship
is a
pain in lower limb
is a
lower limb structure
is a
is a
calf structure
pain in calf
In another view all the possible
subtype relationships are stated directly
• This is “transitive
closure” view is
useful for
optimization
• It can be computed
from distributed
data
• SNOMED is likely to
release this view
later this year
finding
is a
is a
is a
pain
is a
pain in lower limb
is a
is a
pain in calf
Description Logic Classifiers
• Various DL Classifiers exist
• SNOMED uses the Apelon TDE classifier
– It has some limitations but performs well on
a large database
• Some other classifiers testing in past failed or
became very slow with so many concepts
• Others include
–
–
–
–
FaCT++ (Fast Classifier of Terminologies)
Pellet
CEL
RacerPro
Definition of Normal Forms
• In original RT work, dual independent modeling
required exact agreement on stated definition
– Resulted in unresolved arguments about modeling style
• State most immediate parent concepts only, and only those
relationships that have changed, or
• State proximal primitives only, and all defining relationships
• Defining a normal form allowed different modeling
styles for different purposes or preferences
Spackman KA. Normal forms for description logic expression of clinical concepts in SNOMED RT.
Proceedings/AMIA Annual Symposium. :627-631, 2001.
Illustration of need for normal form:
basal cell carcinoma (BCC) of skin of eyelid
• Multiple different ways to postcoordinate:
–
–
–
–
–
Disorder, M=BCC, T=skin of eyelid
Malignant disorder, M=BCC, T=skin of eyelid
Skin disorder, M=BCC, T=skin of eyelid
Disorder of skin of eyelid, M=BCC
Malignant neoplastic disorder of skin eyelid,
M=BCC
– Basal cell carcinoma (disorder), T=skin of
eyelid
– ...
D
D1
D3
D6
Basal cell
malignancy
D9
Neoplastic
disorder
Malignant
neoplasm
D7
D4
D2
Disorder
of skin
Neoplastic
disorder of skin
Malignant
neoplasm of skin
Basal cell
carcinoma of skin
D11
Disorder
D8
Disorder of skin
of eyelid
Neoplastic disorder
of skin of eyelid
D10 Malignant neoplasm
of skin of eyelid
Basal cell carcinoma
of skin of eyelid
D5
Neoplastic
Morphology
Malignant
Morphology
D
Disorder
M1
Skin structure
T1
M2
D1
Neoplastic
disorder
D2
Disorder
of skin
T2
M3 BCC
Morphology
D6
D3
Basal cell
malignancy
D9
Malignant
neoplasm
D7
D4
Malignant
neoplasm of skin
Basal cell
carcinoma of skin
D11
Neoplastic
disorder of skin
D8
Disorder of skin
of eyelid
Neoplastic disorder
of skin of eyelid
D10 Malignant neoplasm
of skin of eyelid
Basal cell carcinoma
of skin of eyelid
D5
Skin
structure
of eyelid
Neoplastic
Morphology
Malignant
Morphology
D
Disorder
M1
Skin structure
T1
M2
D1
Neoplastic
disorder
D2
Disorder
of skin
T2
M3 BCC
Morphology
D6
D3
Basal cell
malignancy
D9
Malignant
neoplasm
D7
D4
Malignant
neoplasm of skin
Basal cell
carcinoma of skin
D11
Neoplastic
disorder of skin
D8
Disorder of skin
of eyelid
Neoplastic disorder
of skin of eyelid
D10 Malignant neoplasm
of skin of eyelid
Basal cell carcinoma
of skin of eyelid
D5
Skin
structure
of eyelid
Neoplastic
Morphology
D
Disorder
M1
Skin structure
T1
Malignant
Morphology M2
D1
Neoplastic
disorder
D2
Disorder
of skin
T2
M3
BCC
Morphology
D6
D3
Malignant
neoplasm
Basal cell
carcinoma
D7
D9
D4
Malignant
neoplasm of skin
Basal cell
carcinoma of skin
Basal cell carcinoma D11
of skin of eyelid
Neoplastic
disorder of skin
D8
D5
Disorder of skin
of eyelid
Neoplastic disorder
of skin of eyelid
D10 Malignant neoplasm
of skin of eyelid
Skin
structure
of eyelid
Neoplastic
Morphology
D
Disorder
M1
Skin structure
T1
Malignant
Morphology M2
D1
Neoplastic
disorder
D2
Disorder
of skin
T2
M3
BCC
Morphology
D6
D3
Basal cell
malignancy
D9
Malignant
neoplasm
D7
D4
Malignant
neoplasm of skin
Basal cell
carcinoma of skin
D11
Neoplastic
disorder of skin
D8
D5
Skin
structure
of eyelid
Disorder of skin
of eyelid
Neoplastic disorder
of skin of eyelid
D10 Malignant neoplasm
of skin of eyelid
Basal cell carcinoma
of skin of eyelid
Note that D1 through
D11 are all sufficiently
defined (non-primitive)
Neoplastic
Morphology
D
Disorder
M1
Skin structure
T1
Malignant
Morphology M2
D1
Neoplastic
disorder
D2
BCC
Morphology
D3
Malignant
neoplasm
D4
Basal cell
malignancy
Neoplastic
disorder of skin
M=M1
T=T1
M=M2
D6
D7
Malignant
neoplasm of skin
M=M2
T=T1
M=M3
D9
Basal cell
carcinoma of skin
M=M3
T=T1
D5
Skin
structure
of eyelid
Disorder of skin
of eyelid
T=T2
D8
Neoplastic disorder
of skin of eyelid
M=M1
T=T2
D10 Malignant neoplasm
of skin of eyelid
M=M2
Attributes
T=T2
Basal cell carcinoma
of skin of eyelid
M=M3 T=T2
D11
T2
T=T1
M=M1
M3
Disorder
of skin
and values
are all inherited
downwards (redundant
ones are removed)
Neoplastic
Morphology
Malignant
Morphology
D
Disorder
M1
Skin structure
T1
M2
D1
Neoplastic
disorder
D2
Disorder
of skin
T2
M3 BCC
Morphology
D6
D3
Basal cell
malignancy
D9
Malignant
neoplasm
D7
D4
Neoplastic
disorder of skin
Malignant
neoplasm of skin
Basal cell
carcinoma of skin
D10
D8
D5
Skin
structure
of eyelid
Disorder of skin
of eyelid
Neoplastic disorder
of skin of eyelid
Malignant neoplasm
of skin of eyelid
Some valid forms for D11:
D9 and T=T2
D11
Basal cell carcinoma
of skin of eyelid
D10 and M=M3
D6 and D5
Neoplastic
Morphology
D
Disorder
M1
Skin structure
T1
Malignant
Morphology M2
D1
Neoplastic
disorder
D2
Disorder
of skin
T2
M3
BCC
Morphology
D6
D3
Basal cell
malignancy
D9
Malignant
neoplasm
D7
D4
Neoplastic
disorder of skin
Malignant
neoplasm of skin
Basal cell
carcinoma of skin
D10
D8
D5
Skin
structure
of eyelid
Disorder of skin
of eyelid
Neoplastic disorder
of skin of eyelid
Malignant neoplasm
of skin of eyelid
Some valid forms for D11:
D9 and T=T2
D11
Basal cell carcinoma
of skin of eyelid
D10 and M=M3
D6 and D5
Neoplastic
Morphology
D
Disorder
M1
Skin structure
T1
Malignant
Morphology M2
D1
Neoplastic
disorder
D2
Disorder
of skin
T2
M3
BCC
Morphology
D6
D3
Basal cell
malignancy
D9
Malignant
neoplasm
D7
D4
Neoplastic
disorder of skin
Malignant
neoplasm of skin
Basal cell
carcinoma of skin
D10
D8
D5
Skin
structure
of eyelid
Disorder of skin
of eyelid
Neoplastic disorder
of skin of eyelid
Malignant neoplasm
of skin of eyelid
Some valid forms for D11:
D9 and T=T2
D11
Basal cell carcinoma
of skin of eyelid
D10 and M=M3
D6 and D5
Summary of BCC example:
• Because of the many different valid forms, it is
useful to define a “normal form” to which we can
transform all expressions for comparison
• In this case, the normal form combines the
proximate primitive (disorder) with the nonredundant existential restrictions
disorder :
finding-site = skin of eyelid (body structure),
associated-morphology = basal cell carcinoma
(morphologic abnormality)
CONCEPT MODEL
Procedure concept model (1)
Procedure site
Procedure site - direct
Anatomical structure,
Acquired body structure
Procedure site - indirect
Procedure Morphology
Indirect morphology
Procedure
Morphologically
abnormal structure
Direct morphology
Procedure device
Direct device
Device
Indirect device
Using device
Using access device
Endoscope and subtypes
Method
Action
Direct substance
Substance, Pharmaceutical /
biologic product
Procedure concept model (1)
Procedure site
Procedure site - direct
Direct
Object
Anatomical structure,
Acquired body structure
Procedure site - indirect
Procedure Morphology
Indirect morphology
Procedure
Morphologically
abnormal structure
Direct morphology
Procedure device
Direct device
Device
Indirect device
Using device
Using access device
Endoscope and subtypes
Method
Action
Direct substance
Substance, Pharmaceutical /
biologic product
Procedure concept model (2)
Procedure
Using substance
Substances
Using energy
Physical forces
Surgical Approach
Procedural approaches
Priority
Priorities
Has focus
Clinical findings, Procedures
Has intent
Intents
Recipient category
Person, Family, Community, Donor, Group
Route of administration
Route of administration value
Revision status
Revision-value, Part of multistage
procedure, Primary operation
Access
Open, closed, percutaneous
(surgical access values)
Attributes
Procedures (1)
• Method
– The action being performed to accomplish the procedure
• Does not include access (e.g., percutaneous) or approach (e.g., translumbar)
– Values are subtypes of “action”
• Procedure site
• Values are subtypes of “anatomical concept”
– Procedure site – direct
• The site directly affected by the action
– Procedure site – indirect
• A site that is acted on but is not the direct target of the action
appendectomy
G
method
excision - action
procedure site - direct
appendix structure
Attributes
Procedures (2)
• Morphology
• Values are subtypes of “morphologically abnormal structure”
– Direct morphology
• The morphology to which the procedure is directed
– Indirect morphology
• A morphology that is acted upon, but is not the direct target of
the action being performed
Evacuation of intracerebral hematoma
G
method
evacuation - action
procedure site - indirect
cerebral structure
direct morphology
hematoma
Attributes
Procedures (3)
• Device
• Values are subtypes of “device”
– Direct device
• The device to which the procedure is directed.
– Indirect device
• A device that is acted upon, but which is not the direct
target of the action being performed
Replacement of electronic heart device battery
direct device
pacemaker battery
indirect device
cardiac pacemaker
Attributes
Procedures (4)
• Using device
– Specifies the instrument utilized to perform the procedure
– Value are subtypes of “device”
core needle biopsy of larynx
Using device

core biopsy needle
Using access device
– Specifies the instrument utilized to gain access to the site
– Value are subtypes of “endoscope”
Endoscopic biopsy
Using access device
endoscope
Attributes
Procedures (5)
• Using energy
– Specifies the energy utilized to perform the procedure
– Value are subtypes of “physical force”
gamma ray therapy
Using energy
gamma radiation
Attributes
Procedures (6)
• Access
– Used to distinguish open, closed, endoscopic, and percutaneous
procedures
• Only used if access is specified in the name of the concept
– Values are subtypes of “surgical access values”
open reduction of fracture
access
open approach
 Surgical Approach
– Specifies the directional, relational or spatial access to the
site of a surgical procedure.
– Values are subtypes of “surgical approach”
transurethral laser prostatectomy
Surgical approach
transurethral approach
Measurement procedure concept model
Component
Cell structure, organism,
substance, observable
Property
Property of
measurement
Time Aspect
Time frame
Scale Type
Nominal, narrative, ordinal,
Quantitative, qualitative,
Text, ordinal OR quantitative
Has Specimen
Specimen
Measurement method
Laboratory procedure
categorized by method
Measurement
Procedure
Attributes
Measurement procedures
• Has specimen
– Specifies the type of specimen on which a measurement or
observation is performed
– Values are subtypes of “specimen”
• Component
– Specifies the substance or observable being observed or
measured by a procedure
– Values are subtypes of “substance”, “observable entity”, or “cell
structure”
creatinine measurement, 24 hour urine
has specimen
24 hour urine sample
component
creatinine
Clinical finding concept model (1)
Finding site
Acquired body structure, Anatomical concepts
Associated morphology
Associated with
Clinical
finding
Morphologically abnormal structure
Clinical finding, Substance, Physical object, Physical force,
Events, Organisms, Pharmacological / Biological product,
Procedure
After
Clinical finding, Procedure
Due to
Clinical finding, Event
Causative agent
Organism, Substance,
Physical object, Physical force
Has interpretation
Findings values, Result comments
Interprets
Laboratory procedure, Observable entity, Patient
evaluation procedure
Clinical finding concept model (2)
Clinical
finding
Pathological process
Pathological process
Clinical Course
Courses
Has definitional manifestation
Clinical finding
Occurrence
Periods of life
Severity
Mild, Moderate, Severe
Episodicity
First episode, New episode,
Ongoing episode
Finding method
Procedure
Finding informer
Performer of method, Subject of record
Provider of history other than subject,
Subject of record or other provider of history
Attributes
Clinical Findings (1)
• Associated morphology
– Specifies morphologic change seen at tissue or cellular level as
a characteristic feature or the disease
– Values are subtypes of “morphologically abnormal structure”
• Finding site
– Specifies the body site affected by a condition
– Values are subtypes of “anatomical concept”
open fracture of femur
associated morphology
fracture, open
finding site
bone structure of femur
Attributes
Clinical Findings (2)
• Causative agent
– Specifies the direct causative agent of the disease
• Does not include vectors (e.g. not mosquito for malaria)
– Values are subtypes of “organisms”, “substances”,
“physical objects” or “physical forces”
bacterial pneumonia
causative agent
bacteria
Attributes
Clinical Findings (3)
• Due to
– Used when one finding/disorder is a cause of another finding, disorder
or procedure. Differs from Causative agent in that the cause is not an
Organism, Substance, Physical Object, or Physical agent
– Values are subtypes of “clinical findings”
diabetic retinopathy
• After
due to
diabetes mellitus
– Specifies a temporal relationship between a Finding/Disorder and a
Finding, Disorder, or Procedure when there is not necessarily a
causal relationship
– Values are subtypes of “clinical findings” or “procedures”
post-viral disorder
after
viral disease
Attributes
Clinical Findings (4)

Clinical Course
– Specifies the course of a condition.
– Used for acute and chronic conditions.
– Not used to refer to rapidity of onset or severity of a
condition.
– Values are subtypes of “courses”
acute myocardial infarction
Clinical course
acute
Attributes
Clinical Findings (5)
• Episodicity
– Specifies the particular episode of a finding that may recur
– If an episode is not the initial episode and is not an ongoing
episode, it is considered a new episode
– Values are “first episode”, “new episode” & “ongoing episode”
new onset angina
episodicity
•
new episode
Severity
– Specifies the level of severity for a Disease concept.
– Values are “mild”, “moderate”, and “severe”
severe vertigo
severity
severe
Attributes
Clinical Findings (6)
• Interprets
– Specifies the “observable entity” or “function” being evaluated or
interpreted by a finding.
– Values are subtypes of “observable entity”, “biological function” or
“measurement procedure”
• Has interpretation
– Specifies the judgement being made about an observable or
function (e.g., presence, absence, degree, normality, etc.)
– Values are subtypes of “findings values” or “result comments”
decreased cardiac output
has interpretation
decreased
interprets
cardiac output
Situation concept model
Associated finding
Associated procedure
Clinical finding; or
Observable / Observation with result
Procedure
Finding context value
Finding context
Situation with
explicit context
• Present, absent, possible
• Unknown
• Goal, risk, etc
Context values for actions
Procedure context
• Done, not done
• Planned, requested
Temporal context value
Temporal context
• Current
• Past, etc
Subject relationship value
Subject relationship context
• Subject of record
• Family member, etc
Specimen concept model
Specimen
Specimen procedure
Procedure
Specimen source topography
Body structure
Specimen source morphology
Morphology
Specimen substance
Substance
Specimen source identity
Person, Family, Donor, Device,
Environment, Community
Pharmaceutical/Biologic Product
concept model
Pharmaceutical /
Biologic product
Has active ingredient
Has dose form
Substance (substance)
Type of drug preparation (product)
Body structure concept model
Body structure
Laterality
Left, Right, Right and left, (unilateral)
Part-of
Body structure
Note: use of “unilateral” implies one side and not the other.
This is a type of negation, and therefore unilateral procedures and unilateral findings
actually must be in the situation hierarchy.
RIGHT IDENTITIES
Right identity
(restricted role value maps)
• RS⊑R
• x Ry
⋀
y Sz → x Rz
• allergyToAspirin v 9 causativeAgent.aspirinSubstance
• aspirinProduct v 9 hasActiveIngredient.aspirinSubstance
• allergyToAspirinProduct v 9 causativeAgent.aspirinProduct
• Allows the automated inference that:
– allergyToAspirinProduct v allergyToAspirin
Right identity
(restricted role value maps)
• RS⊑R
• x Ry
⋀
y Sz → x Rz
• femurFracture v 9 site.femur
• headOfFemurFracture v 9 site.headOfFemur
• headOfFemur v 9 part-of.Femur
• Allows the automated inference that:
– headOfFemurFracture v FemurFracture
• But this isn’t the purpose for which we use right identity in the current
release !
Avoiding Right Identities by Using SEP Triplets
Liver Structure
XM0Ps Liver structure
T-62000 Liver
Liver Part
T-D0535 Liver part
Lobe of liver
Entire liver
7N330 Liver
ROLE GROUPS
Role Groups
• Certain defining relationships may be grouped
to indicate the way they relate to each other
• For example:
– Laparoscopy inspects the peritoneal cavity
– Appendicectomy excises the appendix
– Laparoscopic appendicectomy
• inspects the peritoneal cavity
and
• excises the appendix
but does not
• Excise the peritoneal cavity
– Grouping action and site avoids
misinterpretation
Role groups
• Another example
Cholecystectomy and exploration of bile duct
Method: Excision
Procedure site: Gallbladder
Method: Exploration
Procedure site: Bile duct
Role group 1
Role group 2
Role Grouping as a Compromise
• Implementers and modelers fear/loathe nesting of
expressions
– Nesting violates simple flat frame-based model
• Reality demands faithful representation
• Role grouping attempted (with partial success) to
hide the complexity
– But it was misunderstood by some in DL community as
being a proprietary hack
Spackman KA, Dionne R, Mays E, Weis J. Role grouping as an extension to the description logic of Ontylog
motivated by concept modeling in SNOMED. Proceedings/AMIA Annual Symposium. :712-716, 2002.
Need for Role Groups
• When a single concept may have more than one value for a
particular attribute
– for example, “bone fusion with tendon transfer”
• method = fusion, site = bone, and
• method = transfer, site = tendon
• And, one attribute-value pair needs to be associated with
another.
– How can we specify that the fusion is done to the bone and
not to the tendon? and that the transfer is done to the
tendon and not to the bone?
Role Groups as a Solution
• Informally:
– don’t nest or create sub procedures
– simply “group” the attribute-value pairs
• Using curly braces as a syntactic marker:
{ site=bone, method=fusion},
{site=tendon, method=transfer}
• Or, in tabular form, use a “group” column:
attr
value
group
site
bone
1
method fusion
1
site
tendon
2
method transfer 2
Role Grouping Logical Form:
A Nested Existential Restriction
• C ⊑ 9 RRG :(9R1:C1 ⊓ 9R2:C2) ⊓ 9 RRG :(9R3:C3 )
• Distributed as three 4-tuples in relationships table:
C R3 C3 0
C R1 C1 1
C R2 C2 1
– Role group numbers are arbitrary integers, and not designed to be
stable across changes in the concept definition
ROLE HIERARCHIES
Role (attribute) hierarchies
• Selected SNOMED CT attributes have a hierarchical
relationship to one another known as “role
hierarchies.” In a role hierarchy, one general
attribute is the parent of one or more specific
subtypes of that attribute. Concepts defined using
the more general attribute can inherit concepts
modeled with the more specific subtypes of that
attribute.
Role hierarchies – procedures
• PROCEDURE DEVICE
–
–
–
–
DIRECT DEVICE
INDIRECT DEVICE
USING DEVICE
USING ACCESS DEVICE
• PROCEDURE MORPHOLOGY
– DIRECT MORPHOLOGY
– INDIRECT MORPHOLOGY
• PROCEDURE SITE
– PROCEDURE SITE - DIRECT
– PROCEDURE SITE - INDIRECT
Role hierarchies – clinical findings
• ASSOCIATED WITH role hierarchy:
• ASSOCIATED WITH
– AFTER
– DUE TO
– CAUSATIVE AGENT
Summary of SNOMED’s use of DL
SNOMED
version
Concept & RoleRole
forming Operators axioms
Language
Role
grouping
Early work
(1996-1999)
(⊓, 9R:C)( )
EL
No
SNOMED RT
(2000-2001)
(⊓, 9R:C)(+)
EL+
No
EL
Yes
ELH+
Yes
SNOMED CT (⊓, 9R:C)( )
(Jan02-Jan04)
SNOMED CT (⊓, 9R:C)(+)
(Jul04-present)
R⊑ S
Notation mostly follows Donini in Ch.3 Description Logic Handbook
(+) means right identities were used
CONTEXT MODEL
Common patterns
• Caveat: these are intended for illustrative
purposes only, as examples of ways that
system builders might simplify postcoordination for their clinical experts
• They are full logical models for which the
meaning can be represented in the interface
and in storage with more than one split
between the information model and
terminology model
Common patterns
•
•
•
•
•
•
•
•
•
•
•
Clinical finding present
Clinical finding absent
Clinical finding unknown
History of
No history of
Family history of
No family history of
Observable + value
Procedure done
Procedure not done
(Drug or procedure) contraindicated
•
Plus:
– all the above with site, or site & laterality
Clinical finding present
Situation
Associated finding
Finding context
<finding>
Known present
Group
Temporal context
Subject relationship
context
Clinical-finding-present (<finding>)
Current
Current
or specified time
Subject of record
Abrasion of upper limb
Situation
Associated finding
Finding context
Abrasion of upper limb
Known present
Group
Temporal context
Subject relationship
context
Clinical-finding-present (abrasion of upper limb)
Current
Current
or specified time
Subject of record
Clinical finding absent
Situation
Associated finding
Finding context
<finding>
Known absent
Group
Temporal context
Subject relationship
context
Clinical-finding-absent (<finding>)
Current
Current
or specified time
Subject of record
No chest retractions
Situation
Associated finding
Finding context
Chest wall retraction
Known absent
Group
Temporal context
Subject relationship
context
Clinical-finding-absent (chest wall retraction)
Current
Current
or specified time
Subject of record
Clinical finding unknown
Situation
Associated finding
Finding context
<finding>
Unknown
Group
Temporal context
Subject relationship
context
Clinical-finding-unknown (<finding>)
Current
Current
or specified time
Subject of record
Splenomegaly: unknown
Situation
Associated finding
Finding context
splenomegaly
Unknown
Group
Temporal context
Subject relationship
context
Clinical-finding-unknown (splenomegaly)
Current
Current
or specified time
Subject of record
History of <finding>
Situation
Associated finding
Finding context
<finding>
Known present
Group
Temporal context
Subject relationship
context
History-of (<finding>)
Current
In the past
Subject of record
History of MI
Situation
Associated finding
Finding context
Myocardial
infarction
Known present
Group
Temporal context
Subject relationship
context
History-of (myocardial infarction)
Current
In the past
Subject of record
No history of <finding>
Situation
Associated finding
Finding context
<finding>
Known absent
Group
Temporal context
Subject relationship
context
No-history-of (<finding>)
Current
All times past
Subject of record
No history of seizure
Situation
Associated finding
Finding context
seizure (finding)
Known absent
Group
Temporal context
Subject relationship
context
No-history-of (seizure (finding) )
Current
All times past
Subject of record
Family history of <finding>
Situation
Associated finding
Finding context
<finding>
Known present
Group
Temporal context
Subject relationship
context
Family-history-of (<finding>)
Current
In the past
Family member
Family history of ischemic heart disease
Situation
Associated finding
Finding context
Ischemic heart
disease
Known present
Group
Temporal context
Subject relationship
context
Family-history-of (ischemic heart disease)
Current
In the past
Family member
No family history of <finding>
Situation
Associated finding
Finding context
<finding>
Known absent
Group
Temporal context
Subject relationship
context
No-family-history-of (<finding>)
Current
All times past
Family member
No family history of dementia
Situation
Associated finding
Finding context
dementia
Known absent
Group
Temporal context
Subject relationship
context
No-family-history-of (dementia)
Current
All times past
Family member
Switch to procedures
• Slightly different:
– Two attributes:
• Associated-procedure
• Procedure-context
• Same:
– Temporal context
– Subject relationship context
Procedure done
Situation
Associated
procedure
Procedure
context
<procedure>
Done
Group
Temporal context
Subject relationship
context
Procedure-done (<procedure>)
Current
Current
or specified time
Subject of record
Tetanus booster given
Situation
Associated
procedure
Procedure
context
Booster tetanus
vaccination (procedure)
Done
Group
Temporal context
Subject relationship
context
Procedure-done (booster tetanus vaccination)
Current
Current
or specified time
Subject of record
Procedure not done
Situation
Associated
procedure
Procedure
context
<procedure>
Not done
Group
Temporal context
Subject relationship
context
Procedure-not-done (<procedure>)
Current
Current
or specified time
Subject of record
Neurological examination not done
Situation
Associated
procedure
Procedure
context
Neurological examination
(procedure)
Not done
Group
Temporal context
Subject relationship
context
Procedure-not-done (neurological examination)
Current
Current
or specified time
Subject of record
Drug contraindicated
Situation
Associated procedure
Direct-substance
Procedure context
Administration
of medication
<substance>
Contraindicated
Group
Temporal context
Subject relationship
context
Drug-contraindicated (<substance>)
Current or specified time
Subject of record
Warfarin contraindicated
Situation
Associated procedure
Direct-substance
Procedure context
Administration
of medication
warfarin
Contraindicated
Group
Temporal context
Subject relationship
context
Drug-contraindicated (warfarin)
Current or specified time
Subject of record
Add site to previous patterns
•
•
•
•
Clinical finding present + site
Clinical finding present + site + laterality
Clinical finding absent + site
Clinical finding absent + site + laterality
Clinical finding present + site
Situation
Associated
finding
<finding>
Finding-site
<site>
Group
Finding context
Temporal context
Subject relationship context
Known present
Current or specified time
Subject of record
Clinical-finding-present-with-site (<finding>,<site>)
Bleeding finger
Situation
Associated
finding
bleeding
Finding-site
Finger structure
Group
Finding context
Temporal context
Subject relationship context
Known present
Current or specified time
Subject of record
Clinical-finding-present-with-site (bleeding, finger structure)
Bleeding index finger
Situation
Associated
finding
bleeding
Finding-site
Index finger
structure
Group
Finding context
Temporal context
Subject relationship context
Known present
Current or specified time
Subject of record
Clinical-finding-present-with-site (bleeding,index finger structure)
Clinical finding absent + site
Situation
Associated
finding
<finding>
Finding-site
<site>
Group
Finding context
Temporal context
Subject relationship context
Known absent
Current or specified time
Subject of record
Clinical-finding-absent-with-site (<finding>,<site>)
No right femoral bruit
Situation
Associated
finding
Femoral bruit
Finding-site
Right femoral
artery
Group
Finding context
Temporal context
Subject relationship context
Known absent
Current or specified time
Subject of record
Clinical-finding-absent-with-site (femoral bruit,right femoral artery)
No right femoral bruit
Situation
Associated
finding
bruit
Finding-site
Right femoral
artery
Group
Finding context
Temporal context
Subject relationship context
Known absent
Current or specified time
Subject of record
Clinical-finding-absent-with-site (bruit,right femoral artery)
Clinical finding present + site + side
Situation
Associated
finding
<finding>
Finding-site
<site>
Laterality
Group
Finding context
Temporal context
Subject relationship context
<side>
Known present
Current or specified time
Subject of record
Clinical-finding-present-with-site-and-side (<finding>,<site>,<side>)
Right femoral bruit present
Situation
Associated
finding
bruit
Finding-site
Femoral artery
Laterality
Group
Finding context
Temporal context
Subject relationship context
Right
Known present
Current or specified time
Subject of record
Clinical-finding-present-with-site-and-side (bruit, femoral artery, right)
Clinical finding present + site + side
Situation
Associated
finding
<finding>
Finding-site
<site>
Laterality
Group
Finding context
Temporal context
Subject relationship context
<side>
Known present
Current or specified time
Subject of record
Clinical-finding-present-with-site-and-side (<finding>,<site>,<side>)
Bleeding of left index finger present
Situation
Associated
finding
bleeding
index finger
structure
Finding-site
Laterality
Group
Finding context
Temporal context
Subject relationship context
left
Known present
Current or specified time
Subject of record
Clinical-finding-present-with-site-and-side (bleeding, index finger structure, left)
Bleeding skin, left index finger
Situation
Associated
finding
bleeding
Finding-site
Skin of index finger
Laterality
Group
Finding context
Temporal context
Subject relationship context
Clinical-finding-present-with-site-and-side
(bleeding, skin of index finger, left)
Known present
Current or specified time
Subject of record
left
Clinical finding absent + site + side
Situation
Associated
finding
<finding>
Finding-site
<site>
Laterality
Group
Finding context
Temporal context
Subject relationship context
<side>
Known absent
Current or specified time
Subject of record
Clinical-finding-absent-with-site-and-side (<finding>,<site>,<side>)
No right femoral bruit
Situation
Associated
finding
bruit
Finding-site
Femoral artery
Laterality
Group
Finding context
Temporal context
Subject relationship context
Known absent
Current or specified time
Subject of record
Clinical-finding-absent-with-site-and-side (bruit, femoral artery, right)
right
Using observables
• Finding present + observable + value
Finding present, observable + value
Situation
Associated
finding
Clinical finding
Group
Has-interpretation
Interprets
<value>
<observable>
Group
Finding context
Temporal context
Subject relationship context
Known present
Current or specified time
Subject of record
Finding-present-observable-value (<observable>,<value>)
Knee jerk reflex 2+ (out of 4)
Situation
Associated
finding
Clinical finding
Group
Has-interpretation
++
260348001
Interprets
Knee jerk reflex
Group
271714006
Finding context
Temporal context
Subject relationship context
Known present
Current or specified time
Subject of record
Finding-present-observable-value (knee jerk reflex,++)
Finding present, observable + site + value
(assuming we approve a site attribute for observables)
Situation
Associated
finding
Clinical finding
Group
Has-interpretation
Interprets
<value>
<observable>
Group
obs-site
Finding context
Temporal context
Subject relationship context
<site>
Known present
Current or specified time
Subject of record
Finding-present-obs-site-value (<observable>,<site>,<value>)
Left knee jerk reflex ++
(assuming we approve a site attribute for observables)
Situation
Associated
finding
Clinical finding
Group
Has-interpretation
Interprets
++
Knee jerk reflex
Group
obs-site
Finding context
Temporal context
Subject relationship context
Left knee
Known present
Current or specified time
Subject of record
Finding-present-obs-site-value (knee jerk reflex, left knee,++)
Procedure patterns
• Procedure done + method + site
• Procedure done + method + site + laterality
Procedure done, method+site
Situation
Associated
procedure
procedure
Group
Procedure site - direct
Method
Group
Procedure context
Temporal context
Subject relationship context
<site>
<method>
Done
Current or specified time
Subject of record
Procedure-done-plus-method-site (<method>,<site>)
X-ray of wrist done
Situation
Associated
procedure
procedure
Group
Procedure site - direct
Method
Group
Procedure context
Temporal context
Subject relationship context
Radiographic
imaging
Done
Current or specified time
Subject of record
Procedure-done-plus-method-site (x-ray, wrist)
wrist
Procedure done, method+site+side
Situation
Associated
procedure
procedure
Group
Procedure site - direct
Laterality
<site>
<side>
Group
Method
Procedure context
Temporal context
Subject relationship context
<method>
Done
Current or specified time
Subject of record
Procedure-done-method-site-side (<method>,<site>,<side>)
X-ray of left wrist done
Situation
Associated
procedure
procedure
Group
Procedure site - direct
Laterality
Group
Method
Procedure context
Temporal context
Subject relationship context
Procedure-done-method-site-side
(radiographic imaging, wrist, left)
left
radiographic
imaging
Done
Current or specified time
Subject of record
wrist
More specific patterns also possible
• For example:
– Suture of skin done + site + laterality
Suture of skin done, site + side
Situation
Associated
procedure
Closure of skin by suture
Group
Proceduresite-indirect
<site>
Laterality
Group
Proceduremorphology-direct
Method
Temporal context
Subject relationship context
Laceration
Closure by device
Using-device
Procedure context
<side>
Suture
Done
Current or specified time
Subject of record
Suture of laceration of skin of left index finger: done
Situation
Associated
procedure
Closure of skin by suture
Group
Proceduresite-indirect
Skin of index
finger
Laterality
Group
Proceduremorphology-direct
Method
Temporal context
Subject relationship context
Laceration
Closure by device
Using-device
Procedure context
left
Suture
Done
Current or specified time
Subject of record
REDESIGN PROJECTS
Anatomy Redesign
• Reintroduce part-of roles
• Sufficiently define the S and P of the SEP triad
• Align the E with the Foundational Model of Anatomy
(FMA)
Reflexive roles
• Plan to introduce reflexive “part-of” as a way of
handling “SEP” model evolution
proper-part-of v part-of
² v part-of
S ´ 9 part-of . E
P ´ 9 proper-part-of . E
Suntisrivaraporn B, Baader F, Schulz S, and Spackman K. Replacing SEP-Triplets in SNOMED CT using Tractable Description
Logic Operators. In Jim Hunter Riccardo Bellazzi, Ameen Abu-Hanna, editor, Proceedings of the 11th Conference on Artificial
Intelligence in Medicine (AIME'07), Lecture Notes in Computer Science. Springer-Verlag, 287-291, 2007.
Substance Redesign
• Remove inappropriate is-a relationships
• Add new attributes and values
• Many difficult decisions remain
– How to represent “may be used as …”
• Timolol may be used as an eye medicine for glaucoma
• Timolol may be used as a cardiac beta blocker
– Are H2CO3 and HCO3- the same or different? Do we
represent the various anions of drugs also?
organism redesign
• Major steps:
1. organize SNOMED's taxonomy into a
systematic and consistent Linnean
hierarchy
2. remove all non-taxonomic information
about living organisms from the
taxonomic hierarchy
3. represent such information, when
understandable, reproducible and useful,
elsewhere in the terminology
Current taxonomy includes:
• Linnean taxonomic terms (“Canis
familiaris”)
• Common names for organisms (“Dog”)
• Non-taxonomic information
– Use and Circumstances
• Laboratory fur-bearing animal
– Pathogenicity
• Parasite, pyogenic bacterium
– Life cycle stage of organisms
• Worm eggs
Common names in the FSN:
• Some organisms have many common names
– Butorides virescens = green heron, green-backed heron,
little green heron, crab-catcher, fly-up-the-creek, green
bittern, poke, shitepoke, skeow, skow, and swamp
squaggin
• May be impossible to verify what organism is
meant
– Ex: Comte de Paris star frontlet (organism) ???
• A single common name may refer to more than
one species:
– Ex: Yellowhammer (organism) MAY BE A Emberiza
citrinella, MAY BE A Colaptes auratus
Non-taxonomic terms in a taxonomic hierarchy:
• a subtype is always and necessarily a "kind of" its
parent (this is what subsumption means)
• interpolation of non-taxonomic terms in a
taxonomic hierarchy violates this convention
– these terms are often context-dependent rather than
defining.
• An elephant may be a domestic animal in India
• a dog may be a food animal in Korea
• Is a canary a “Wild bird--chordate” or a “Domestic
fowl”?
– Answer: Neither. It is Serinus canaria
Further enhancements of the model
• Attributes & values to represent contextual
information about living organisms
– Contexts of domesticity (domestic, feral, wild)
– Contexts of use (food, laboratory, companion, service,
breeding, etc)
– Contexts of life stage (oocyst, larva, spore, trophozooite,
etc)
– Contexts of medical significance (parasite, renotrophic
organism, pathogen)???
Qualifiers for organisms?
• An organism might be qualified by non-taxonomic
attributes/values, just as a disease might be
qualified by severity, stage, episode, degree of
control, etc.
• But:
– Type 2 diabetes that is out of control is not really a different
type of disease; it is a different type of situation in which
type 2 diabetes (the disease) is present.
– Dairy cattle and beef cattle are not really different types of
organisms; they are different types of contexts in which
cattle (organisms) are used for different purposes.
What about “infectious agents”?
• The taxonomy of parasites, bacteria and other
potentially pathogenic microorganisms is also a
mixture of scientific names, common names, and
contextual information
• Attempting to convey “contexts of pathogenicity”
creates errors in logic:
– Ex: Helminth ISA Parasite?
• Wrong. Most helminths are not parasitic
– Ex: Fungus ISA Infectious agent?
•
Wrong. Most fungi are not infectious
Observable Redesign
• Separate processes, functions, and qualities
• Add attributes that define observables in terms of:
–
–
–
–
Properties they observe
Timing
Scales or units
Techniques of observation
• Add attributes that define qualities/properties in
terms of:
– the independent continuant in which they inhere
DRAFT model of observables
Observable entity
COMPONENT
Substances, functions, processes,
activities, organisms, cell structures
PROPERTY
Properties
SYSTEM /
OBSERVABLE SITE
Specimens, Sites
TIME ASPECT
Time aspects
SCALE / UNITS
Scale types, units
TECHNIQUE
techniques
DRAFT alternative model of observables
Observable entity
PROPERTY
INHERES IN
TOWARDS
properties
independent continuant
Functions, substances
TIME ASPECT
Time aspects
SCALE / UNITS
Scale types, units
TECHNIQUE
techniques
Events, conditions, episodes
• Need to define what is an event, what is a condition,
what is an episode
• Need criteria for deciding whether we need one
code or two
–
–
–
–
seizure, epilepsy: clearly different, so we need two codes
tachycardia, tachyarrhythmia: same or different?
low hemoglobin, anemia: same or different?
rash of forearm: do we need both a disorder and a finding?
Should we add more expressive DL features?
•
•
•
•
•
•
•
•
•
General concept inclusion axioms
Transitive roles
Reflexive roles
Disjointness axioms
Value restrictions
Negation
Disjunction
Cyclic definitions
Number restrictions
General concept inclusion axioms
• Extremely useful feature
• Compatible with a polynomial-time structural
subsumption algorithm
• Allows us to say what is true in addition to what is
sufficient
– Gastric ulcer is located in the stomach, and in addition it
necessarily involves the gastric mucosa
Transitive roles
• x Ry ⋀ y Rz → x Rz
• Useful for causal/associational chains
• Interaction with role hierarchy is interesting &
useful
• Example: Associated-with-after
– Varicella (chicken pox)
– An infection with causative-agent = varicella virus
– Herpes zoster
– Also has causative-agent = varicella virus, and occurs after
varicella
– Post-herpetic neuralgia
– Occurs after herpes zoster (therefore occurs after varicella),
but is not an infection with causative-agent varicella virus
Reflexive roles
• Plan to introduce reflexive “part-of” as a way of
handling “SEP” model evolution
proper-part-of v part-of
² v part-of
S ´ 9 part-of . E
P ´ 9 proper-part-of . E
Suntisrivaraporn B, Baader F, Schulz S, and Spackman K. Replacing SEP-Triplets in SNOMED CT using Tractable Description
Logic Operators. In Jim Hunter Riccardo Bellazzi, Ameen Abu-Hanna, editor, Proceedings of the 11th Conference on Artificial
Intelligence in Medicine (AIME'07), Lecture Notes in Computer Science. Springer-Verlag, 287-291, 2007.
Value restriction 8R:C
• Not an intuitive construct
– person u 8hasCar:Jaguar
– Includes people who have no car, but if they had one it
would have to be a Jaguar . . . . Do we encounter this kind
of concept in common-sense thinking?
• Creates pernicious interactions with disjunction and negation
that tend to make structural subsumption algorithms
incomplete
• But it was included in ALC and FL, so languages including it
were studied extensively.
Negation :C
• Head injury without loss of consciousness
headInjury u : lossOfConsciousness
situation u
9 includes.headinjury u
: 9 includes.lossOfConsciousness
Disjunction C t D
• Some high-level aggregators are naturally
disjunctive
• We can address this need partially by using
navigation hierarchies
Cyclic definitions, number restrictions
• ? No significant need for these at present
INFORMATION MODEL
INTERACTIONS
Interaction between Terminology and Information Models
Clinical Decision Support Model
+ Inference Rules
Terminology Model
+ Compositional Expressions
Information Model
+ Patient Data Structures
Diagram based on Figure 1 in Rector AL et al. “Interface of Inference Models with
Concept and Medical Record Models” AIME 2001: 314-323
Terminology vs Information model
What’s the issue?
• Information model:
– Determines and organizes the kinds of entities which
carry values in a record
– Loosely referred to as slots, facets, fields, questions
• Terminology model:
– Determines and organizes the kinds of entities which
are the values
– Variously referred to as the terminology or ontology
or value sets
Extremes (reductio ad absurdum)
• Put all meaning in the terminology
– A code (or terminology expression) for every meaning
that needs to be expressed
– Only one “field” in the record
• What about dates, numeric values, names, places,
and relationships between them?
• Put all meaning in the information model
– Two values: “yes” and “no”
– A “question” for every meaning that needs to be
expressed, and a field for every question
• What about combinatorial explosion of subtypes of
things in the real world?
Representing the semantics of clinical data
• For any given application
– There needs to be a boundary between information
model and terminology
• without gaps or overlaps
• There are several different choices for where to
draw the boundary between
• No single choice of boundary is globally the best
• How can we achieve standardization for
interoperability?
Terminology – information model interaction:
broad tasks required
• Identify gaps and overlaps
• Design a strategy to
– Fill the gaps
– Manage the overlaps
• Demonstrate implementability
TermInfo – Specific advice
Act.code & Observation.value (1)
•
HL7 theory
–
–
•
code  nature of observation
value  value of the observation
Practical implementation
–
Simple for numeric observations
–
Reasonable for observations with coded results
•
•
–
Hemoglobin level (code) = 14g/dL (value)
Visual acuity (code) = Can count fingers (value)
Tricky for observations where the distinction between
the nature and value is arbitrary
•
“Blood group AB” could be …
1. ABO Blood grouping (code) Blood group AB
(value)
2. Blood group A antigen (code) Present (value)
and Blood group B antigen (code) Present
(value)
3. Blood group AB (code)
TermInfo – Specific advice
Act.code & Observation.value (2)
•
•
The code-value split is even more
arbitrary for general clinical
observations
For example
– Finding of abdominal tenderness …
1. Examination (code) abdomen tender (value)
2. Abdominal examination (code) abdomen tender
(value)
3. Abdominal palpation (code) abdomen tender
(value)
4. Abdominal tenderness (code) present (value)
5. Abdomen tender (code)
TermInfo – Specific advice
Code and Value – guidance
•
HL7 code and value distinction should be used for
– Numeric and non-numeric results of measurement
procedures
• A single coded attribute should express the full
semantics
– If there is no non-arbitrary reproducible
distinction
– Recommended Implementation
•
• Act.Code = ASSERTION
• Observation.Value = Coded SNOMED
expression
Rationale
– SNOMED CT clinical findings are
• not just the value of a particular type of
observation
• equivalent to an observable or observation
type with a value
“Code-value” discussion
• Not unique to HL7
• Suggests terminology-information model
standardization efforts may benefit from each other