MarcCUGGIA,SaharBayat,DelphineRossille,PatricePoulain, - - PowerPoint PPT Presentation

Marc
CUGGIA,
Sahar
Bayat,
Delphine
Rossille,
Patrice
Poulain,

 Patrick
Pladys,
Régis
Duvauferrier
 Inserm
U936

 Conceptual
Modelling
of
Biomedical
Knowledge
 Faculté
de
Médecine
–
Rennes
‐
France


IntroducKon


Seman&c
Interoperability


Knowledge
 Models
 InformaKon
 Models


InformaKon
 System
A
 InformaKon
 System
B


IntroducKon


Knowledge
 Models
 InformaKon
 Models
 Terminologies
 Ontologies
 ICD,
RxNorm,
 Snomed,
 LOINC…
 Interoperability
 standards
 HL7,
EN13606,
 OpenEHR,
DICOM,
 HPRIM…


• Terms
et
concepts
of
the
domain

• DefiniKons
:
ontologies

• InformaKon
structure

• messages
or
documents


IntroducKon


Knowledge
 Models
 InformaKon
 Models
 Rector,
A.
L.
(2001).
"The
Interface
between
 Informa;on,
Terminology,
and
Inference
 Models."
STUDIES
IN
HEALTH
TECHNOLOGY
 AND
INFORMATICS:
246‐250.
 Interface
 RepresentaKon
 formalism
?
 Terminology/ontolology
 Data
element


ObjecKves


To
Compare
two
relevant
informaKon
standards


 HL7
and
Open
EHR
 according
to
 ‐
The
informa&on
representa&on
formalism

 ‐
The
interface
with
the
knowledge
models


Materiel
and
Method


• Apgar
score
:
To
assess
the
health
of
newborns
just


a\er
childbirth


• Score
range
0
to
10

• Measured
at
1,
5,
10
minutes


Virginia
Apgar


Score
value


Pulse
rate
 Breathing
 Muscle
Tone
 Reflex
irritability
 Skin
color


0


<80
 absent
 none
 No
response
to
 sKmulaKon
 cyanosis


1


80
‐
100
 Weak
or
 irregular
 Some
flexion
 Grimace
cry
 when
sKmulated
 acrocyanosis


2


>
100
 strong
 AcKve
 movement
 vive
 pink


Participation

Act Role Link

Act Relationship

Entity Role

DMIM
 refinement
 Messages
:
 Business
process
 Documents
structures
 (CDA
&
Template)


HL7
v3


Reference
 InformaKon
 Model
 (RIM)
 Domain
 Message
 InformaKon
 Model
 (DMIM)


Pulse
rate
=
 Breathing=
 Muscle
Tone
=
 Reflex=

 Skin
Color=


APGAR
Score
 ECG
 report
 Weight
 Birth
 weight
 Adult
 weight
 MenstruaKon
 Menstrual
 Cycle
 Pain


Refinement


Reference
 Model


Archetypes


inheritance
 composiKon


OpenEHR


Material
and
Method


Archetype
Apgar
score
(2008)
 published
by
(2008)
 
«
APGAR
score
»
part

of
the
 perinatal
DMIM
published
by
 Goosen
(2005)


Method


• EvaluaKon
criteria:


– What
is
the
degree
of
formalism
of
each
model
?
 – Is
the
context
expressed
in
the
model
?
 – Could
we
use
the
model
in
another
context
?
 – How
is
the
binding
with
knowledge
models


Composi&on


Birth
data


APGAR
 SCORE


Birth
 Weight


No
birth
or
perinatal
 context
in
the
OpenEHR
 Taxonomy


No
link
between
 pulse
rate
and
apgar
 score
archetype


Header
:
archetype
metadata
:

 purpose,
use,
authors
(free
text)
 Temporality
 Data
DefiniKon
 No
hierarchy
between
the
 data
elements
(e.g
TOTAL)


Apgar
Score
 HL7
V3
 OpenEHR
 Formalism
 degree


• Formal
structure

• Complexity

• lack
of
organizaKon
in
the


archetypes
hierarchy


• Few
defined
relaKonships


between
the
data
elements
 Reusability


• limited

• Possibility
of
modularizaKon


(CMET
:

generic
model
of
score)


• Modularity
like
Lego
Bricks


Context

 Binding


• Context
is
“embedded”

• 2
data
linked
to
LOINC

• No
relaKonship
to
the
context

• 2
data
linked
to
LOINC


Study
of
the
InformaKon
Models

 
ComparaKve
analyses
of
the
representaKons



Participatio n

Act Role Link

Act Relationsh ip

Entity Role

RIM
 Informa&on
 Model
 Knowledge
 Models
 LOINC
 Muscle
Tonus


The
terminology
binding


Muscle
tone=
<


Participatio n

Act Role Link

Act Relationsh ip

Entity Role

RIM
 LOINC
 Muscle
Tone


How
to
handle
the
binding
to
 different
Knowledge
Models?


Observable
 Finding
 Muscle
Tone
 MuscleTone
 SNOMED
 Informa&on
 Model
 Knowledge
 Models
 Muscle
tone=
<


Participatio n

Act Role Link

Act Relationsh ip

Entity Role

RIM
 LOINC
 Muscle
Tone


How
to
handle
the
binding
to
 different
Knowledge
Models?


Observable
 Finding
 Muscle
Tone
 MuscleTone
 SNOMED
 Informa&on
 Model
 Knowledge
 Models
 Muscle
tone=
<
 GREY
ZONE


Discussion
&
conclusion


• Limits
:
1
example
but
which
illustrates
generic


problems


• Gap
between
informaKon
and
knowledge
model
:


– 
QuanKtaKve
aspect

to
increase
the
binding
 – QualitaKve
aspect
:


• “Easy”
to
solve
if
terminology
is
considered
as
a
flat


model
(TermInfo
IniKaKve)


• Need
to
re‐think
the
modeling
process
of
both
models


in
a
same
Kme


• More
reusable
structures
of
informaKon
and
a
beoer

• rganizaKon
of
data
elements.