Transcript Document

R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Introduction to Medical Informatics
Terminology and Ontology
Challenges
SUNY at Buffalo - December 13, 2010
Werner CEUSTERS
Center of Excellence in Bioinformatics and Life Sciences
Ontology Research Group
University at Buffalo, NY, USA
http://www.org.buffalo.edu/RTU
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Focus of the case study
• Information technology (IT) in support of local
clinical operations.
• Goal: provide clinicians with IT tools that allow
them to do their job:
– more efficiently, through:
• computerized order entry
• work-flow support
• standardized in-house electronic communication
– and with less risks for errors, through:
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• automated decision support.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
What is not discussed
• Structured medical reporting,
• Coding of health data,
• Semantic interoperability with IT systems in other
healthcare facilities that provide care to the same
patients,
• N-ary use of the data for clinical research,
prevention, drug discovery, …
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R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A more modern view
Everything collected wherever, whenever and
about whomever which is relevant to a medical
problem in whomever, whenever and
wherever, should be accessible without loss of
relevant detail.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Is this scary?
• The misuse of medical records has led
to loss of jobs, discrimination, identity
theft and embarrassment.
– An Atlanta truck driver lost his job after his
insurance company told his employer that he had
sought treatment for alcoholism.
– A pharmacist disclosed to a California woman that
her ex-spouse was HIV positive, information she
later used against him in a custody battle.
– A 30-year employee of the FBI was forced into
early retirement when the FBI found his mental
health prescription records while investigating the
man’s therapist for fraud.
http://www.consumer-action.org/
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Requires ‘agents’ (in at least 2 meanings)
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Is this desirable? (2005)
• An estimated thirty to forty cents of every United
States’ dollar spent on healthcare, or more than a
half-trillion dollars per year, is spent on costs
associated with ‘overuse, underuse, misuse,
duplication, system failures, unnecessary
repetition, poor communication, and inefficiency’.
– Proctor P. Reid, W. Dale Compton, Jerome H. Grossman, and Gary Fanjiang, Editors
(2005) Building a Better Delivery System: A New Engineering/Health Care
Partnership. Committee on Engineering and the Health Care System, National
Academies Press.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Is this desirable? (2006)
• At least 1.5 million preventable adverse drug
events occur in the United States each year.
– Institute of Medicine. Preventing Medication Errors. 2006
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Is this possible?
• There are already so many amazing technologies
available or ready for clinical trial:
–
–
–
–
–
–
Smart pills that send emails when taken,
‘Blood bots’ for endovascular surgery,
Thought-controlled artificial limbs,
‘Breathalyzer’ for disease diagnosis,
Implantable nano wires to monitor blood pressure,
…
R T U New York State
Center ofifExcellence
in is not just on ‘doing’
It is possible
the focus
Bioinformatics & Life Sciences
comparing
acting
representing
observing
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
This brings new requirements
• Data should not only be understandable by
humans, but also by machines.
– natural language is a hard nut to crack for machines.
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R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Language is ambiguous
• Often we can figure it out …
warning on plastic bag
in Miami hotel lobby
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Language is ambiguous
• Sometimes, we can not
…
in Amsterdam hotel elevator
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
The problem of reference in free text
• ‘The surgeon examined Maria. She found a small
tumor on the left side of her liver. She had it
removed three weeks later.’
• Ambiguities:
–
–
–
–
who denotes the first ‘she’: the surgeon or Maria ?
on whose liver was the tumor found ?
who denotes the second ‘she’: the surgeon or Maria ?
what was removed: the tumor or the liver ?
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
This brings new requirements
• Data should not only be understandable by
humans, but also by machines.
– natural language is a hard nut to crack for machines.
• Data should be unambiguous:
– clinicians can deal with certain ambiguities, but
machines can not.
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R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Coding systems used naively preserve certain ambiguities
PtID
Date
ObsCode
Narrative
5572
04/07/1990
26442006
closed fracture of shaft of femur
5572
04/07/1990
81134009
Fracture, closed, spiral
5572
12/07/1990
26442006
closed fracture of shaft of femur
5572
12/07/1990
9001224
Accident in public building (supermarket)
5572
04/07/1990
79001
Essential hypertension
0939
24/12/1991
255174002
benign polyp of biliary tract
2309
21/03/1992
26442006
closed fracture of shaft of femur
2309
21/03/1992
9001224
Accident in public building (supermarket)
47804
03/04/1993
58298795
Other lesion on other specified region
5572
17/05/1993
79001
Essential hypertension
298
22/08/1993
2909872
Closed fracture of radial head
298
22/08/1993
9001224
Accident in public building (supermarket)
5572
01/04/1997
26442006
closed fracture of shaft of femur
5572
01/04/1997
79001
Essential hypertension
0939
20/12/1998
255087006
malignant polyp of biliary tract
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Codes for ‘types’ AND identifiers for instances
PtID
Date
ObsCode
Narrative
5572
04/07/1990
26442006
IUI-001
closed fracture of shaft of femur
5572
04/07/1990
81134009
IUI-001
Fracture, closed, spiral
5572
12/07/1990
26442006
IUI-001
closed fracture of shaft of femur
5572
12/07/1990
9001224
IUI-007
Accident in public building (supermarket)
5572
04/07/1990
79001
IUI-005
Essential hypertension
0939
24/12/1991
255174002
IUI-004
benign polyp of biliary tract
2309
21/03/1992
26442006
IUI-002
closed fracture of shaft of femur
2309
21/03/1992
9001224
IUI-007
Accident in public building (supermarket)
47804
03/04/1993
58298795
IUI-006
Other lesion on other specified region
5572
17/05/1993
79001
IUI-005
Essential hypertension
298
22/08/1993
2909872
IUI-003
Closed fracture of radial head
298
22/08/1993
9001224
IUI-007
Accident in public building (supermarket)
5572
01/04/1997
26442006
IUI-012
closed fracture of shaft of femur
5572
01/04/1997
79001
IUI-005
Essential hypertension
IUI-004
malignant polyp of biliary tract
7 distinct
disorders255087006
0939
20/12/1998
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
The solution: terminology + ontology
• Terminology:
– solves certain issues related to language use, i.e. with respect to
how we talk about entities in reality (if any);
• Relations between terms / concepts
– does not provide an adequate means to represent independent of
use what we talk about, i.e. how reality is structured;
• Women, Fire and Dangerous Things (Lakoff).
• Ontology (of the right sort):
– Language and perception neutral view on reality.
• Relations between entities in first-order reality
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R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Terminology alone gets it often wrong
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Africa [Z01.058] +
Americas [Z01.107] +
Antarctic Regions [Z01.158]
Arctic Regions [Z01.208]
Asia [Z01.252] +
Atlantic Islands [Z01.295] +
Australia [Z01.338] +
Cities [Z01.433] +
Europe [Z01.542] +
Historical Geographic Locations
[Z01.586] +
Indian Ocean Islands [Z01.600] +
Oceania [Z01.678] +
Oceans and Seas [Z01.756] +
Pacific Islands [Z01.782] +
•
•
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•
•
•
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•
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•
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Ancient Lands [Z01.586.035] +
Austria-Hungary [Z01.586.117]
Commonwealth of Independent States
[Z01.586.200] +
Czechoslovakia [Z01.586.250] +
European Union [Z01.586.300]
Germany [Z01.586.315] +
Korea [Z01.586.407]
Middle East [Z01.586.500] +
New Guinea [Z01.586.650]
Ottoman Empire [Z01.586.687]
Prussia [Z01.586.725]
Russia (Pre-1917) [Z01.586.800]
USSR [Z01.586.950] +
Yugoslavia [Z01.586.980] +
R T U New York State
Center of Excellence in
Terminology
gets it
Bioinformatics &alone
Life Sciences
often wrong
All MeSH Categories
Diseases Category
Nervous System Diseases
Male Urogenital
Diseases
Eye Diseases
Cranial Nerve
Diseases
Optic Nerve
Diseases
Eye Diseases,
Hereditary
Optic Nerve
Diseases
Optic Atrophy
Female Urogenital Diseases
and Pregnancy Complications
Female Urogenital Diseases
Neurodegenerative
Diseases
Heredodegenerative
Disorders,
Nervous System
Urologic Diseases
Kidney Diseases
Optic Atrophies,
Hereditary
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Wolfram
Syndrome
Diabetes Insipidus
R T U New York State
Center of Excellence in
What would
it mean if used in the context of a patient ?
Bioinformatics & Life Sciences
All MeSH Categories
???
Diseases Category
Nervous System Diseases
Male Urogenital
Diseases
Eye Diseases
Cranial Nerve
Diseases
Optic Nerve
Diseases
Eye Diseases,
Hereditary
has
…
Optic Nerve
Diseases
Optic Atrophy
Female Urogenital Diseases
and Pregnancy Complications
Female Urogenital Diseases
Neurodegenerative
Diseases
Heredodegenerative
Disorders,
Nervous System
Urologic Diseases
Kidney Diseases
Optic Atrophies,
Hereditary
has
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Wolfram
Syndrome
Diabetes Insipidus
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Snomed CT (July 2007):
“fractured nasal bones”
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R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
SNOMED-CT: abundance of false synonymy
bones
nose
fracture
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R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Coding / Classification confusion
A patient with a fractured nasal bone
=
A patient with a broken nose
=
A patient with a fracture of the nose
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R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Coding / Classification confusion
A patient with a fractured nasal bone
=
=
A patient with a broken nose
=
=
A patient with a fracture of the nose
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R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
‘Ontology’
• In philosophy:
– Ontology (no plural) is the study of what entities exist and how they
relate to each other;
• In computer science and many biomedical informatics
applications:
– An ontology (plural: ontologies) is a shared and agreed upon
conceptualization of a domain;
• The realist view within the Ontology Research Group
combines the two:
– We use Ontological Realism, a specific methodology that uses
ontology as the basis for building high quality ontologies, using
reality as benchmark.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Terminological versus Ontological approach
• The terminologist defines:
– ‘a clinical drug is a pharmaceutical product given to (or taken
by) a patient with a therapeutic or diagnostic intent’. (RxNorm)
• The ontologist thinks:
– Does ‘given’ includes ‘prescribed’?
– Is manufactured with the intent to … not sufficient?
• Are newly marketed products – available in the pharmacy, but not yet
prescribed – not clinical drugs?
• Are products stolen from a pharmacy not clinical drugs?
• What about such products taken by persons that are not patients?
– e.g. children mistaking tablets for candies.
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R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Observations and similarities
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A useful parallel: Alberti’s grid
Ontological
theory
representation
reality
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
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L3
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
L2
L1
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R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A (trivial?) example: diagnosis versus disease
The diagnosis is here
The disease is here
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Conclusion
• The challenges:
– Revision of the appropriatness of concept-based
terminology for specific purposes;
– Relationship between models and that part of reality
that the models want to represent;
– Adequacy of current tools and languages for
representation;
– Boundaries between terminology and ontology and the
place of each in semantic interoperability.