Transcript Realist Ontology for Electronic Healthcare Records

ECO
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European Centre for
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Realist Ontology for
Electronic Healthcare Records
Dr. Werner Ceusters, MD
European Centre for Ontological Research
Universität des Saarlandes
Saarbrücken, Germany
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R Electronic Healthcare Record
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• A collection of electronic data about a single
patient relevant for his health.
• Exists in many forms with various degrees of
automation:
– Scanned documents
– “Machine readable” documents (text, XML,...)
– Personal (GP), departmental, hospital wide, ...
• Multiple challenges:
–
–
–
–
Deontological: safety, security, confidentiality
Technical: structure and architecture, communication
Pragmatic: getting them used
Machine interpretable: triggering and allerts
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Focus of this presentation
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• Role of ontology in maximizing the potential
uses of the EHCR ... :
– For the patient’s own benefit
– For the advance of science
– Hence, for the health of the population
• ... by making the contents understandable
both for humans and machines.
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Understanding content (1)
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“John Doe has a pyogenic
granuloma of the left thumb”
John Doe has a
pyogenic
granuloma of
the left thumb
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Understanding content (2)
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<record>
<patient>John Doe</patient>
<diagnosis>pyogenic granuloma of the left thumb</diagnosis>
</record>
<record>
<subject> John Doe </subject>
<diagnosis> pyogenic granuloma
of the left thumb </diagnosis>
</record>
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Understanding content (3)
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<129465004>
<116154003>John Doe</116154003>
< 8319008 > 17372009
<finding site> 76505004
<laterality>7771000</laterality>
</finding site>
</ 8319008 >
</129465004>
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Current “state of the art” on
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meaning in healthcare informatics
• A pervasive bias towards “concepts”
– Content wise:
• Work based on ISO/TC37 that advocates the
Ogden-Richards theory of meaning
• Corresponds with a linguistic reading of “concept”
– Architecture wise:
• In Europe: work based on CEN/TC251 WG1 & WG2
that follow ISO/TC37
• In the US: HL7, inspired by Speech Act Theory
• “Concepts” used as elements of information models,
hence mixing a linguistic and engineering reading.
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Topics
• Meaning and understanding
• Biomedical terminologies and concept
systems
• EHCR architecture
• Problems of the concept-based approach
• Ontology as part of the solution
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Dyadic models of “meaning”
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• Saussure (language philosopher):
– signe / signifiant
(sign/concept)
• Ron Stamper (information scientist):
– thing-A STANDS-FOR thing-B
• Major drawback:
– excludes the “referent” from the model, i.e. that
what the sign/symbol/word/... denotes
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Triadic models of meaning:
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The Semiotic/Semantic triangle
Reference:
Concept / Sense / Model / View / Partition
Sign:
Language/
Term/
Symbol
Referent:
Reality/
Object
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Aristotle’s triadic meaning
model
pathema
semeia

gramma/ phoné
Words spoken are signs or symbols
(symbola) of affections or impressions
(pathemata) of the soul (psyche); written
words (graphomena) are the signs of words
spoken (phoné). As writing (grammatta), so
also is speech not the same for all races of
men. But the mental affections themselves,
of which these words are primarily signs
(semeia), are the same for the whole of
mankind, as are also the objects
(pragmata) of which those affections are
representations or likenesses, images,
copies (homoiomata).
pragma
Aristotle, 'On Interpretation', 1.16.a.4-9,
Translated by Cooke & Tredennick,
Loeb Classical Library,
William Heinemann, London, UK, 1938.
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Richards’ semantic triangle
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reference
my understanding
symbol
your understanding
referent
• Reference (“concept”):
“indicates the realm of
memory where
recollections of past
experiences and
contexts occur”.
• Hence: as with Aristotle,
the reference is “mindrelated”: thought.
• But: not “the same for
all”, rather individual
mind-related
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Don’t confuse with
homonymy !
R1
R2
R3
mole (skin lesion)
mole (unit)
“mole”
mole (animal)
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Different thoughts Homonymy
One concept
understandingof x understanding of y
R1
R2
R3
mole
“skin lesion”
mole
“unit”
symbol
referent
“mole”
mole
“animal”
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And by the way, synonymy...
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Richards’ view
the Aristotelian view
“sweat”
“perspiration”
“sweat”
“perspiration”
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Frege’s view
sense
name
• “sense” is an objective
feature of how words are
used and not a thought or
concept in somebody’s
head
• 2 names with the same
reference can have
different senses
• 2 names with the same
sense have the same
reference (synonyms)
reference • a name with a sense does
not need to have a
(=referent)
reference (“Beethoven’s
10th symphony”)
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Tetrahedric extensions
CEN/TC251
ENV 12264
FRISCO model
(information science)
conception
concept
actor
representation
definition
referent
term
referent
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The theory in practice
Oops, this is
too slippery
to hold any
longer
He wants
me to hold
that kocher
Take this, please !
Nurse
reaches for
the clamp
Haydom Lutheran Hospital, Tanzania
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Issues in communication
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=?
Take the
kocher, please.
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Concept-based Terminology
kocher
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HL7-RIM
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Living subject
person
nonPersonLS
Place
Organisation
Material
ManufacteredM
Device
Container
Managed PatientEncounter
Employee
Participation ControlAct
Patient
LicensedEntity
Supply
Access
Diet
WorkingList
Procedure
Observation
Entity
PublicHealthcare
DiagnosticImage
DeviceTask
SubstanceAdministration
FinancialContract
Account
FinancialTransaction
InvoiceElement
Role
Role
Link
Language
Communication
Participation
ActLink
Communication
Function
Act
Context
Structure
CEN ENV 13606
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CEN’s view on reality and
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the healthcare record
“The real world of health and health care is
made up of individual clinical situations
(of which the participants are called
“associate topics”), that are described by an
EHCR author as clinical statements.
Within an EHCR system each clinical
statement will be expressed as an
elementary healthcare record entry.”
CEN ENV 13606
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EHR Extended Architecture
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Architectural
Component
Record
Component
Root
Architectural
Component
Selected
Component
Complex
Folder
Original
Component
Complex
Headed
Section
Composition
Cluster
Link Item
Data Item
Elementary healthcare record entries
Data
DataItem
Item
Data
Item
Specialisation
Data
Item
Specialisation
Specialisation
Specialisation
CEN ENV 13606
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OCC
specialisation
Description
Examples of Component
Names
Folder
High-level subdivisions of the entire
EHCR for a patient, usually grouping
entries over long time-spans within
one organisation or department, or for
a particular health problem
GP Record
Inpatient Stay
Diabetes Care Record
Composition
A set of record entries relating to one
time and place of care delivery;
grouped contributions to an aspect of
health care activity; composed reports
and overviews of clinical progress
Consultation
Operation Notes
Discharge Summary
Vital Signs Chart
Headed
Section
Sub-divisions used to group entries
with a common theme or derived
through a common healthcare process
Past Medical History
Presenting Symptoms
Examination Findings
Treatment Plan
Cluster
Low-level aggregations of elementary
entries (Record Items) to represent a
compound clinical concept
Heart Sounds
Differential White Cell Count
Insulin Schedule
CEN ENV 13606
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Architectural Component Attributes
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Refer to situations and statements and rely on terminology
Language
Component
unique identifier
0..1
1
Related
date and time
0..n
Component
Status information
1
1
Component
name structure
Architectural
Component
0..n
Related
healthcare
agent
1
Originating
Healthcare
agent
1
1
Originating
date and time
0..n
Distribution
Rule Reference
Subject of care
identifier
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CEN ENV 13606
“Archetypes”
• clinical situation
– pertains to body component, product, environment
– has context facet subject of information, process
status, role for dates
– has information qualifier knowing mode
• has information source
• has qualifier
– has qualifier
– has role
• Is stated by
• has temporal marker
actor
communication modality
relevance
role for clinical situation
actor, healthcare organisation
timing marker
To be used to build terminologies that may be used for the EHR
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Structure of concept-based terminologies
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urine
gall bladder
gall
gallbladder
inflammation
bladder
urinary
bladder
biliary cystitis
urine
inflammation
gall
cystitis
inflammation
gallbladder
gallbladder
inflammation
urinary bladder
inflammation
urinary
bladder
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“Axiom”
• Concept-based terminology (and
standardisation thereof) is there as a
mechanism to improve understanding of
messages, originally by humans, now also
by machines.
• It is NOT the right device to explain why
reality is what it is, how it is organised, etc.,
(although it is needed to allow us to
communicate on insights thereof).
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Why not ?
• Ad hoc readings of statements of the type C1-relationshipC2
– Human has-part head
// Human has-part finger
– California is-part-of United States // California isa name
– labial vein isa vein of head
// labial vein isa vulval vein
• Concepts not necessarily correspond to something that
(will) exist(ed)
– Sorcerer, unicorn, leprechaun, ...
• Definitions set the conditions under which terms may be
used, and may not be abused as conditions an entity must
satisfy to be what it is
• Language can make strings of words look as if it were
terms
– “Middle lobe of left lung”
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What is then the right way ?
Realist Ontology !
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If, later, you can
remember just one thing
of this presentation, then
make sure it is this one:
If somebody uses the word
“ontology”, ALWAYS let him be
specific about what he
understands by it.
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The O-word
N. Guarino, P. Giaretta, "Ontologies and Knowledge Bases: Towards a
Terminological Clarification". In Towards Very Large Knowledge Bases:
Knowledge Building and Knowledge Sharing, N. Mars (ed.), pp 25-32. IOS
Press, Amsterdam, 1995.
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“Ontology”
• In Information Science:
– “An ontology is a
description (like a formal
specification of a program)
of the concepts and
relationships that can exist
for an agent or a community
of agents.”
• In Philosophy:
– “Ontology is the science of
what is, of the kinds and
structures of objects,
properties, events,
processes and relations in
every area of reality.”
concept
definition
term
referent
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My use of the word ontology
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a for a computer understandable
representation of some pre-existing
The T-Boxreflecting
has no
domain of REALITY,
the
meaning without
to be
used its
by
properties of the objects
within
the A-Box
software
in a
domain in such a way
that(agents)
there obtain
NOT
by
• does not
rely systematic
onmachine,
what people and
know
or
substantial
and
correlations
think, hence no “concepts”,
not just
humans
between
reality and the ontology itself.
epistemology
• instance driven, although it accepts
universals that are not instanciated
modified from Barry Smith
• does not “create” or “constrain” reality
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R Back to to the operating theatre
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A lot of
objects present
This
surgeon
with some relations
Part of
This mask
This
amputatio
n stump
This hand
Haydom Lutheran Hospital, Tanzania
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R Back to to the operating theatre
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A lot of
processes going on
This wound
being closed
by holding ...
with some relations
Part of
That wound
fluid
drained
This kocher
being held in
that hand of
that surgeon
Haydom Lutheran Hospital, Tanzania
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“Axiom”
epistemology
• If the picture is not a fake, we (i.e., me and
this audience) KNOW that that hand, that
surgeon, ... EXIST(ed), i.e. ARE (were)
REAL.
• But importantly: that hand, surgeon, kocher,
mask, ... EXIST(ed) independent of our
knowledge about them and also the partrelationship between that hand and that
surgeon, and the processes going on, are
(were) equally real.
ontology
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(Simplified) Logic of classes
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• primitive:
– entities: particulars versus universals
– relation inst such that:
• all classes are universals; all instances are
particulars
• some universals are not classes, hence have no
instances: pet, adult, physician
• some particulars are not instances; e.g. some
mereological sums
• subsumption defined resorting to instances:
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Basic Formal Ontology
Basic Formal Ontology consists in a series
of sub-ontologies (most properly conceived as
a series of perspectives on reality), the most
important of which are:
– SnapBFO, a series of snapshot ontologies (Oti ),
indexed by times
– SpanBFO a single videoscopic ontology (Ov).
Each Oti is an inventory of all entities existing
at a time. Ov is an inventory (processory) of all
processes unfolding through time.
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Alan Rector
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Take home message:
a need for a clean separation of
knowledge AND ontology
Pragmatic knowledge: what users usually say or think, what they
consider important, how to integrate in software
Knowledge of classification and coding systems: how an expression has
been classified by such a system
Knowledge of definitions and
criteria: how to determine if a
concept applies to a particular instance
Surface linguistic knowledge:
how to express the concepts in
any given language
Conceptual knowledge: the knowledge of sensible domain concepts
Ontology: what exists and how what exists
relates to each other