Ontological Realism for Biomedical Ontologies and Electronic Health - - PowerPoint PPT Presentation

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August 29, 2011; 11.30 AM – 01.00 PM Radisson Blu Scandinavia Hotel Oslo, Norway

• W. CEUSTERS 1 , M. BROCHHAUSEN 2, S. SCHULZ 3

1 Ontology Research Group, and Department of Psychiatry, University at Buffalo, NY 2 University of Arkansas for Medical Sciences, Little Rock, AR 3 Institut für Medizinische Informatik Statistik und Dokumentation, Medical University,

Graz, Austria. Tutorial-style Workshop

Ontological Realism for Biomedical Ontologies and Electronic Health Records

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Program

• Ontology and ontologies (5 min, WC): the distinction between

‘Ontology’ as scientific discipline and as representational artifacts;

• Ontological Realism & ontologies (25 min, MB):

– Ontological Realism as basis for upper ontologies, focusing on BFO. – Entities and relations recognized by Ontological Realism (e.g. ACGT), – Quality criteria for OBO Foundry Ontologies;

• Ontology authoring and evaluation (30 min, SS): application of
• ntological realism in detecting and avoiding common mistakes in

formal representations;

• Ontological Realism and EHRs (20 min, WC): how to improve

EHR data by means of ontological realism;

• Roundup and future collaborations (10 min)

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• W. Ceusters

The distinction between ‘ontology’ as scientific discipline and ‘ontologies’ as representational artifacts

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What’s in a name?

• 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 of the presenters combines the two:

– We use Ontological Realism, a specific methodology that uses

• ntology as the basis for building high quality ontologies, using

reality as benchmark.

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Semantic Applications

use Ontologies and Software

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Domain

‘Philosophical’ approach to

• ntology

Ontologies Ontology Authoring Tools Reasoners

create

Computer Science approach to ‘ontology’

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• 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.

The basic axioms of Ontological Realism

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

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Representations First Order Reality

L1: entities with

• bjective existence

L2: clinicians’ beliefs about (1) L3: linguistic representations about (1), (2) or (3)

Three levels of reality in Ontological Realism

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• bservation &

measurement

A crucial distinction: data and what they are about

data

• rganization

model development use add

Generic beliefs

verify further R&D

(instrument and study optimization)

application

Δ =

• utcome

First- Order Reality Representation

i s a b

• u

t

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Distinguish what is generic and what is specific. L1. First-order reality

• L2. Beliefs

(knowledge)

Generic Specific

DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MOLECULE PERSON DISEASE PATHOLOGICAL STRUCTURE PORTION OF INSULIN DRUG me my blood glucose level my NIDDM my doctor my doctor’s computer

L3. Representation ‘person’ ‘drug’ ‘insulin’ ‘W. Ceusters’ ‘my sugar’

Referent Tracking Realism-based Ontology

Generic Specific

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(5 min.) The distinction between ‘ontology’ as scientific discipline and ‘ontologies’ as representational artifacts

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Mathias Brochhausen

(25 min.)

Ontological Realism & Ontologies

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Stefan Schulz

(30 min.)

Ontology authoring and evaluation

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(20 min.)

Ontological Realism and EHRs

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The ultimate goal of Healthcare IT

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.

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If it is possible outside healthcare …

received confirmation call

Note in ‘EHR’ about calories purchased (or card blocked?)

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However, there are many fallacies

• 1. Crippled idea about ‘problem list of diagnoses’

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Crippled idea about ‘problem list of diagnoses’

• Basis of Problem List:

– Larry Weed’s Problem Oriented Medical Record

• Each medical record should have a complete list of all the patient's

problems, including both clearly established diagnoses and all other

unexplained findings that are not yet clear manifestations

• f a specific diagnosis.
• Includes:

– diagnosis − physical finding – lab abnormality − physiologic finding – social issue − symptom – demographic issue

Weed LL. Medical records that guide and teach. N Engl J Med. 1968 Mar 14;278(11):593-600.

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However, there are many fallacies

• 1. Crippled idea about ‘problem list of diagnoses’
• 2. Conflation of diagnosis and disease/disorder

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Conflation of diagnosis and disease/disorder The disorder is there The diagnosis is here The disease is there

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However, there are many fallacies

• 1. Crippled idea about ‘problem list of diagnoses’
• 2. Conflation of diagnosis and disease/disorder
• 3. The structure of EHR data (information model) is

not close enough to the structure of that what the data are about

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EHR Information Models (simplified) patient diagnosis drug finding encounter patient diagnosis drug finding

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However, there are many fallacies

• 1. Crippled idea about ‘problem list of diagnoses’
• 2. Conflation of diagnosis and disease/disorder
• 3. The structure of EHR data (information model) is

not close enough to the structure of that what the data are about

• 4. Unjustified belief that the use of unambiguous

codes renders EHR data unambiguous

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R T U Using generic representations for specific entities is inadequate

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

PtID Date SNOMED CT code Narrative

0939 20/12/1998 255087006 malignant polyp of biliary tract

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However, there are many fallacies

• 1. Crippled idea about ‘problem list of diagnoses’
• 2. Conflation of diagnosis and disease/disorder
• 3. The structure of EHR data (information model) is

not close enough to the structure of that what the data are about

• 4. Unjustified belief that the use of unambiguous

codes renders EHR data unambiguous

• 5. Popular ontologies will solve the problems

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Ceusters W. - MIE 2011 25

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A non-trivial relation Referent Reference

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A non-trivial relation Referent Reference Concept ?

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Some key questions

• What referents, if any at all,

are depicted by a putative reference?

• How do changes at the level
• f the referents correspond

with changes in the collection of references?

• If references are transmitted,

how can the receiver know what referents are depicted?

Referent Reference 28

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The problem in a nutshell

• Generic terms used to denote specific entities do not have

enough referential capacity

– Usually enough to convey that some specific entity is denoted, – Not enough to be clear about which one in particular.

• For many ‘important’ entities, unique identifiers are used:

– UPS parcels – Patients in hospitals – VINs on cars – …

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explicit reference to the concrete individual entities relevant to the accurate description of some portion of reality, ... Fundamental goals of ‘our’ Referent Tracking

Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

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Method: numbers instead of words

Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

– Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity

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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

PtID Date ObsCode Narrative

0939 20/12/1998 255087006 malignant polyp of biliary tract IUI-001 IUI-001 IUI-001 IUI-003 IUI-004 IUI-004 IUI-005 IUI-005 IUI-005 IUI-007 IUI-007 IUI-007 IUI-002 IUI-012 IUI-006

7 distinct disorders Codes for ‘types’ AND identifiers for instances

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The shift envisioned

• From:

– ‘this man is a 40 year old patient with a stomach tumor’

• To (something like):

– ‘this-1 on which depend this-2 and this-3 has this-4’, where

• this-1 instanceOf human being …
• this-2 instanceOf age-of-40-years …
• this-2 qualityOf this-1 …
• this-3 instanceOf patient-role …
• this-3 roleOf this-1 …
• this-4 instanceOf tumor …
• this-4 partOf this-5 …
• this-5 instanceOf stomach …
• this-5 partOf this-1 …
• …

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The shift envisioned

• From:

– ‘this man is a 40 year old patient with a stomach tumor’

• To (something like):

– ‘this-1 on which depend this-2 and this-3 has this-4’, where

• this-1 instanceOf human being …
• this-2 instanceOf age-of-40-years …
• this-2 qualityOf this-1 …
• this-3 instanceOf patient-role …
• this-3 roleOf this-1 …
• this-4 instanceOf tumor …
• this-4 partOf this-5 …
• this-5 instanceOf stomach …
• this-5 partOf this-1 …
• …

denotators for particulars

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Ceusters W. - MIE 2011

The shift envisioned

• From:

– ‘this man is a 40 year old patient with a stomach tumor’

• To (something like):

– ‘this-1 on which depend this-2 and this-3 has this-4’, where

• this-1 instanceOf human being …
• this-2 instanceOf age-of-40-years …
• this-2 qualityOf this-1 …
• this-3 instanceOf patient-role …
• this-3 roleOf this-1 …
• this-4 instanceOf tumor …
• this-4 partOf this-5 …
• this-5 instanceOf stomach …
• this-5 partOf this-1 …
• …

denotators for appropriate relations

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Ceusters W. - MIE 2011

The shift envisioned

• From:

– ‘this man is a 40 year old patient with a stomach tumor’

• To (something like):

– ‘this-1 on which depend this-2 and this-3 has this-4’, where

• this-1 instanceOf human being

…

• this-2 instanceOf age-of-40-years

…

• this-2 qualityOf this-1

…

• this-3 instanceOf patient-role

…

• this-3 roleOf this-1

…

• this-4 instanceOf tumor

…

• this-4 partOf this-5

…

• this-5 instanceOf stomach

…

• this-5 partOf this-1

…

• …

denotators for universals

• r particulars

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Ceusters W. - MIE 2011

The shift envisioned

• From:

– ‘this man is a 40 year old patient with a stomach tumor’

• To (something like):

– ‘this-1 on which depend this-2 and this-3 has this-4’, where

• this-1 instanceOf human being

…

• this-2 instanceOf age-of-40-years

…

• this-2 qualityOf this-1

…

• this-3 instanceOf patient-role

…

• this-3 roleOf this-1

…

• this-4 instanceOf tumor

…

• this-4 partOf this-5

…

• this-5 instanceOf stomach

…

• this-5 partOf this-1

…

• …

time stamp in case of continuants

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Ceusters W. - MIE 2011

Relevance: the way RT-compatible EHRs ought to interact with representations of generic portions of reality

instance-of at t

#105

caused by

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Big Picture

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Ceusters W. - MIE 2011

• a disease is a disposition rooted in a physical disorder in the
• rganism and realized in pathological processes.

etiological process disorder disposition pathological process abnormal bodily features signs & symptoms interpretive process diagnosis produces bears realized_in produces participates_in recognized_as produces

Basis of OGMS

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Ceusters W. - MIE 2011

Example: adverse events

• Level 1:

– #1: an incident that happened in the past;

• Level 2:

– #2: the interpretation by some cognitive agent that #1 is an adverse event; – #3: the expectation by some cognitive agent that similar incidents might happen in the future;

• Level 3:

– #4: an entry in the adverse event database concerning #1; – #5: an entry in some other system about #3 for mitigation or prevention purposes.

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Ceusters W. - MIE 2011 42

Allows appropriate error management

• Some possibilities:
• 1. #1with unjustified absence of #2:
• #1 was not perceived at all, or not assessed as being an

adverse event

• 2. Unjustified presence of #2:
• There was no #1 at all, or #1 was not an adverse event
• 3. Unjustified absence of #4
• Same reasons as under (1) above
• Justified presence of #2 but not reported in the database

– …

Ceusters W, Smith B. A Realism-Based Approach to the Evolution of Biomedical Ontologies. Proceedings of AMIA 2006, Washington DC, 2006;:121-125.

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Ceusters W. - MIE 2011 43

Part of the ReMINE Domain Ontology

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Ceusters W. - MIE 2011 44

Higher order logical representation

• an incident (#1) that happened at time t2 to a patient (#2)

after some intervention (#3 at t1)

• is judged at t3 to be an adverse event, thereby giving rise

to a belief (#4) about #1 on

• the part of some person (#5, a caregiver as of time t6).
• This requires the introduction (at t4) of an entry (#6) in

the adverse event database (#7, installed at t0).

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Ceusters W. - MIE 2011 45

Advantages

• Synchronisation of two distinct representations of the

same reality:

– taxonomies:

• user-oriented view
• data annotation
• Domain ontology compatible with OBO-Foundry
• ntologies:

– no overlap, – easier to re-use.

• Not only tracking of incidents, but also:

– how well individual clinicians and organizations manage adverse events, – how well one learns from past experiences. – ontologies:

• realism-based view
• unconstrained reasoning

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All participants

(10 min.)

Questions and Answers

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