Transcript PPT - EuroRec

R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Post-hoc interpretation of mutually
incoherent information models:
the role and benefits of
realism-based ontology.
September 1, 2009, Sarajevo
Werner CEUSTERS
Center of Excellence in Bioinformatics and Life Sciences
Ontology Research Group
University at Buffalo, NY, USA
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
What is an Information Model?
• An information model is:
– ‘a representation of concepts, relationships,
constraints, rules, and operations to specify data
semantics for a chosen domain of discourse that satisfy
some industry need’.
• A ‘quality’ information model is:
– ‘an information model that is complete, sharable,
stable, extensible, well-structured, precise, and
unambiguous’.
Y. Tina Lee. Information Modeling: From Design To Implementation.
http://www.mel.nist.gov/msidlibrary/doc/tina99im.pdf
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Why are there so many IM but no ‘quality’ IM?
• An information model is:
– ‘a representation of concepts, relationships,
constraints, rules, and operations to specify data
semantics for a chosen domain of discourse that satisfy
some industry need’.
• many domains,
• different needs within the same domain,
• selection of ‘concepts’, ‘relationships’, … relevant for the
needs.
can never be complete
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Why are there so many?
Blobel B, Pharow P: Analysis and Evaluation of EHR Approaches. MIE 2008, 26-28 May 2008, Göteborg, Sweden
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Why are so many incompatible?
• An information model is:
– ‘a representation of concepts, relationships,
constraints, rules, and operations to specify data
semantics for a chosen domain of discourse that satisfy
some industry need’.
• confusion about:
– what ‘concepts’ and ‘relationships’ are,
– whether a ‘domain of discourse’ is:
» what is or can be said, versus,
» that about what something is or can be said,
– ‘semantics’.
can never be unambiguous and precise
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
No serious scholar should work with ‘concepts’
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Slow penetration of the idea …
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
More serious scholars become convinced …
what is a concept
description a
description of?
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Crouching patient, Hidden data
Dear Mr. President,
a Data Model for my Electronic Health Records
Nearly Killed Me
Joe Bugajski
Mr. President, your historic economic stimulus package (The American Recovery and
Reinvestment Act of 2009), appropriated $19 billion for health information technology
("Technology Gets a Piece of Stimulus", New York Times, January 25, 2009). This week, your
Director of the Office of Management and Budget (OMB), Peter Orszag, shockingly held that half
of the US operating deficit can disappear with lower healthcare costs and these will obtain
through electronic healthcare records (Daily Show, 6 April 2009). Today, the Wall Street Journal
wrote that you proudly proclaimed that electronic healthcare records for the members of the US
military, like my youngest son, and continuing through Veterans Affairs "will provide a 'seamless
system' to facilitate information sharing and cut red tape, ending the need for veterans to
transfer military records to receive benefits". Whereas Star Wars and Star Gate movie fantasies
provide great fun, witnessing you, a world leader, spew delusional visions of a nation-covering,
interoperable, secure, private, reliable, accurate, and instantaneous electronic healthcare data
network is at best terrifying and at worst pernicious.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Two major problems in information modeling (1)
• Tyranny of the use case:
– ‘if most people wrongly believe that crocodiles are a
kind of mammal, then most people would find it
easier to locate information about crocodiles if it were
located in a mammals grouping, rather than where it
factually belonged’. (p89)
Huhns MN, Stephens LM. Semantic Bridging of Independent
Enterprise Ontologies. In: Kosanke K, ed. Enterprise Inter- and IntraOrganizational Integration: Building International Consensus. Boston,
MA: Kluwer Academic Publishers; 2002:83 – 90.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Two major problems in information modeling (2)
• Assumption of inherent classification:
–
–
we identify every thing by a specific class to which it
belongs; and
there exists a preferred set of classes to describe a domain.
• Sad consequences:
–
–
–
–
‘the complexity of problems in schema integration, schema
evolution, and interoperability,
violates philosophical and cognitive guidelines on
classification and is, therefore,
inappropriate in view of the role of data modeling in
representing knowledge about application domains’.
Parsons, J. and Wand, Y. Emancipating instances from the tyranny of classes in information modeling. ACM Trans.
Database Syst. 25, 2 (June 2000), 228–268.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Both problems have a common ground
• Confusion brought about by the (dis)similarity between
information and what the information is about:
space
}
}
}
}
anamnesis
clinical examination
diagnosis
therapeutic interventions
time
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
OpenEHR Information Model
switching between
data structures and
what the data are
about
T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
HL7 RIM (core)
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
HL7 EHR structure
For HL7,
a
document
is an act !
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
The right sort of ontology can help …
• In computer science:
– a formal specification of a conceptualization
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Not the wrong sort: linking the wrong way…
Martínez-Costa, Menárguez-Tortosa, Fernández-Breis, Maldonado. A model-driven approach for representing clinical archetypes for
Semantic Web environments. Journal of Biomedical Informatics 42(1), February 2009, 150-164
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Not the wrong sort: not every term denotes (1)
• ‘A well-known problem in clinical information recording is the problem of
assigning “status”, including variants like “actual value of P” (P stands for
some phenomenon), “family history of P”, “risk of P”, “fear of P”, as well as
negation of any of these, i.e. “not/no P”, “no history of P” etc.
• A proper analysis of these so called statuses shows that they are not “statuses” at
all, …’
– this is so true !
• ‘… but different categories of information as per the ontology. The common
statement types mentioned here are mapped as follows:
•
•
•
•
•
•
•
actual value of P ⇒ Observation (of P);
no/not P ⇒ Observation (of excluded P or types of P, e.g. allergies).
family history of P ⇒ Evaluation (that patient is at risk of P);
no family history of P ⇒ Evaluation (that P is an excluded risk);
risk of P ⇒ Evaluation (that patient is at risk of P);
no risk of P ⇒ Evaluation (that patient is not at risk of P);
fear of P ⇒ Observation (of FEAR, with P mentioned in the description);’
– some of these P’s do not exist at all !
T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Not the wrong sort: not every term denotes (2)
• ‘Another set of statement types that can be confused in systems that
do not properly model information categories concern
interventions, e.g. “hip replacement (5 years ago)”, “hip
replacement(planned)”, “hip replacement (ordered for next tuesday
10 am)”.’
– this is so true !
• ‘Ambiguity is removed here as well,with the use of the correct
information categories, e.g. (I stands for an intervention):
• I (distant past/unmanaged/passively documented)
– ⇒ Observation (of I present in patient);
• I (recent past) ⇒ Action (of I having been done to/for patient);
• I (proposed) ⇒ Evaluation, subtype Proposal (of I suggested/likely for patient);
• I (ordered) ⇒ Instruction (of I for patient for some date in the future).’
– some of these I’s do not exist at all !
T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Schemas like this need to be corrected
T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
An appropriate view on reality …
T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
An appropriate view on reality …
K Bernstein, M Bruun-Rasmussen, S Vingtoft, SK Andersen, C Nøhr.
Modelling and implementing electronic health records in Denmark.
International Journal of Medical Informatics (2005) 74, 213—220.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
… can still lead to an erroneous ‘ontology’
Clinical Investigator Recording (CIR) ontology
T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
… and to leaving observed distinctions implicit
‘not knowing’
or ‘not
specifying’
something is
not a property
of that what is
not known or
that about what
a specification
should be
given, but a
property of the
agent involved.
T Beale, S Heard, D Kalra, D Lloyd. The openEHR Architecture Support Terminology.
Revision: 1.0.1; 04 Aug 2008
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
The right sort of ontology can help …
• In computer science:
– a formal specification of a conceptualization
• leads to bad ontologies
• In philosophy:
– a representation of reality
• In the OBO Foundry:
– a representational artifact which is intended to
represent universals and some defined classes.
• foundation in philosophical realism
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Basic axioms
1. There is an external reality which
is ‘objectively’ the way it is;
2. That reality is accessible to us;
3. We build in our brains cognitive
representations of reality;
4. We communicate with others
about what is there, and what we
believe there is there.
Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the
Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Two sorts of representations
L1
R
L2
L3
symbolizations
beliefs
‘about’
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
So this is the right framework, though not well implemented
T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
The three levels applied to diabetes management
Generic
3. Representation
2. Beliefs
(knowledge)
1.
First-order
reality
‘person’ ‘drug’
‘insulin’
DIAGNOSIS
INDICATION
PATHOLOGICAL
STRUCTURE
DRUG
MOLECULE
Specific
‘W. Ceusters’
‘my sugar’
my doctor’s
work plan
my doctor’s
diagnosis
my doctor
PERSON
DISEASE
PORTION OF
INSULIN
me
my doctor’s
computer
my NIDDM
my blood glucose
level
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Distinction between Ontologies and Information Models
• Ontologies should represent only what is always
true about the entities of a domain (whether or not
it is known to the person that reports),
• Information models (or data structures) should
only represent the artifacts in which information is
recorded.
– Such information may be incomplete and error-laden
which needs to be accounted for in the information
model rather than in the ontology itself.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Realism-based ontology basics (1)
some continuant
universal
instanceOf at
some continuant
particular
some occurrent
universal
t
instanceOf
some occurrent
particular
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Sorts of relations
UtoU: isa, partOf(UU), …
U1
U2
PtoU:
instanceOf,
lacks,
denotes(PU)…
P1
PtoP: partOf, denotes, …
P2
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
The importance of temporal indexing
malignant
tumor
benign
tumor
instanceOf at t1
instanceOf at t2
partOf at t1
this-4
partOf at t2
stomach
instanceOf at t2
instanceOf at t1
this-1’s stomach
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
The essential pieces
dependent
continuant
material
object
t
history
me
… at t
spatial
region
instanceOf
t
participantOf at t
some
quality
spacetime
region
t
occupies
my
life
my 4D
STR
projectsOn at t
located-in at t
some
spatial
region
temporal
region
projectsOn
some
temporal
region
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Diseases : L1  Diagnoses L2/L3
Diagnosis:
Disease
• A configuration of
representational units;
isa
• Believed to mirror the
Pneumococcal pneumonia
person’s disease;
• Believed to mirror the
Instance-of at t1
disease’s cause;
• Refers to the universal
of which the disease is
#78
#56
caused
John’s portion
John’s
believed to be an
by
of pneumococs
Pneumonia
instance.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Some motivations and consequences (2)
• A diagnosis can be of level 2 or level 3, i.e. either
in the mind of a cognitive agent, or in some
physical form.
• Allows for a clean interpretation of assertions of
the sort ‘these patients have the same diagnosis’:
 The configuration of representational units is such that
the parts which do not refer to the particulars related to
the respective patients, refer to the same portion of
reality.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Perfect ‘semantic’ tools are useless …
• … if data captured at the source is not of high
quality
• Prevailing EHR systems don’t allow data to be
stored at acceptable quality level:
– No formal distinction between disorders and diagnosis
– Messy nature of the notions of ‘problem’ and ‘concern’
– No unique identification of the entities about which
data is stored
• Unique IDs for data-elements cannot serve as unique IDs for
the entities denoted by these data-elements
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Making existing EHR systems RT compatible
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Portion of Reality
Entity
Configuration
represents
Relation
Universal
Particular
contains
is about
Non-referring
particular
class
Information content ent.
denotes
corresponds-to
Representation
RT-tuple
Representational unit
Defined
class
…
…
…
Extension
Denotator
CUI
IUI
UUI
RUI
denotes
denotes
denotes
Representations
in Referent
Tracking
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Relevance: the way RT-compatible systems ought to interact
with representations of generic portions of reality
instance-of at t
caused
#105
by
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
MedtuityEMR Patient’s Encounter Document
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Conclusions
• Current information modeling practices hamper
semantic interoperability;
• Prevailing approaches to ‘ontology’ aren’t much
better;
• There is improvement however:
– some acknowledge the problem, but either
• don’t find the solution, or
• don’t wish to use the solution.
• It takes courage indeed …