Werner CEUSTERS, MD Center of Excellence in Bioinformatics and Life Sciences

New York State Center of Excellence in Bioinformatics & Life Sciences

R T U New York State Center of Excellence in Bioinformatics & Life Sciences

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Clinical Trial Ontology Meeting

How to build an Ontology ?Some basic principles

NIH, May 16-17, 2007

Werner CEUSTERS, MDCenter of Excellence in Bioinformatics and Life Sciences

Department of Psychiatry, University at Buffalo, NY, USA

http://www.org.buffalo.edu/RTU

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Short personal history

1959 - 20061977

1989

1992

19982002

2004

http://www.ifomis.org/

http://images.google.com/imgres?imgurl=http://www.math.sfu.ca/histmath/Europe/Euclid300BC/HIPPOCRATES.JPG&imgrefurl=http://www.math.sfu.ca/histmath/Europe/Euclid300BC/&h=326&w=268&sz=11&tbnid=ETy_g3qaEY0J:&tbnh=113&tbnw=93&start=4&prev=/images%3Fq%3Dhippocrates%26hl%3Dnl%26lr%3D

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Mainstream interpretations of “ontology”• An explicit specification of an agreed upon

conceptualization of a domain– Tom Grüber

• Anything what is given the name ‘ontology’ and that can be described in terms of 6 axes: expressiveness, structure, intended use, granularity, automated reasoning, prescriptive/descriptive– Ontology Summit 2007

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Problems with mainstream ontologies• Based upon the confusing notion of “concept”

– Unit of thought or knowledge concerning anything perceivable or conceivable

– The meaning of a term– …

• Confuse information representation with domain representation

Information about X part_of information about Y

X part of Y

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What I mean with the word “ontology”• A representation of some pre-existing domain of reality (a

portion of reality) which

1. reflects the properties of the entities within its domain in such a way that there obtains a systematic correlation between reality and the representation itself,

2. is intelligible to a domain expert

3. is formalized in a way that allows it to support automatic information processing

reality

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Three levels of reality

1. The world exists ‘as it is’ prior to a cognitive agent’s perception thereof;

2. Cognitive agents build up ‘in their minds’ cognitive representations of the world;

3. To make these representations publicly accessible in some enduring fashion, they create representational artifacts that are fixed in some medium.

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, November 8, 2006, Baltimore MD,

USA

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O

RU1O1

concretization

R1st level reality

Represent what exist and is relevant

BRU1

B1 Cognitiverepresentation

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Some characteristics of representational units1. each unit is assumed by the creators of the

representation to be veridical, i.e. to conform to some relevant POR as conceived on the best current scientific understanding;

2. several units may correspond to the same POR by presenting different though still veridical views or perspectives;

3. what is to be represented by the units in a representation depends on the purposes which the representation is designed to serve.

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Some characteristics of an optimal ontology• Each representational unit in such an ontology

would designate – (1) a single portion of reality (POR), which is – (2) relevant to the purposes of the ontology and such

that – (3) the authors of the ontology intended to use this unit

to designate this POR, and– (4) there would be no PORs objectively relevant to

these purposes that are not referred to in the ontology.

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Three types of ontologies• Upper level ontologies:

– (should) describe the most generic structure of reality• Domain ontologies:

– (should) describe the portion of reality that is dealt with in some domain

– Special case: reference ontologies• Application ontologies:

– To be used in a specific context and to support some specific application

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Clinical trial ontologies• As domain ontologies:

– Cover all entity types relevant in the clinical trial domain

• As application ontologies:– A subset of the above which is large enough to support

all functions the application has to serve:• CT protocol development• Study management• Data analysis• …



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Key question

How to build an optimal clinical trial domain ontology ?



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Rule 1:

Analyze the domain



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Rule 2a:

Try to be lazy:re-use what others have done.

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The BRIDG (domain analysis) model• NOT an ontology• A computable clinical trials protocol

representation – that supports the entire life-cycle of clinical trial

protocols, and– that will serve as a foundation for caBIG modules

• that support all phases of the clinical trials life cycle, (including protocol authoring) and

• be developed to meet user needs and requirements.The BRIDG Project: Creating a model of the semantics of clinical trials research. Douglas B. Fridsma. July 26, 2006

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Reasons for selecting BRIDG

• BRIDG tries to solve an important problem• Does not completely ignore reality as many other

initiatives do: – If the tools and models don’t work with reality, it is

probably the tools and the models that need to change• The BRIDG Project: Creating a model of the semantics of clinical trials research. Douglas B. Fridsma.

July 26, 2006

• Intended to become the next best thing on earth (after HL7, I assume)

(although one has to search hard to find evidence and sometimes it looks as if some contributors observed reality from outer space)

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http://www.bridgproject.org/status.html

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BRIDG_Model_V1_49

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BRIDG model organization

Image from: The BRIDG Project: Creating a model of the semantics of clinical trials research. Douglas B. Fridsma. July 26, 2006



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Rule 2b:

Try to be lazy: re-use what others have done,But… remain critical at all times!

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Being critical ≠ being negative

RFQ-NCI-60001-NG: Review of NCI Thesaurus and Development of Plan to Achieve OBO-Compliance

Grant to Apelon (H. Solbrig) to improve NCIT



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Rule 3:

Don’t have a blind trust in the power of representation and modeling

languages, and certainly not in UML

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‘Death by UML Fever’• It is important to emphasize that UML itself is not the direct cause

of any maladies described herein. • Instead, UML is largely an innocent victim caught in the midst of

poor process, no process, or sheer incompetence of its users. • UML sometimes does amplify the symptoms of some fevers as the

result of the often divine-like aura attached to it. • For example, it is not uncommon for people to believe that no

matter what task they may be engaged in, mere usage of UML somehow legitimizes their efforts or guarantees the value of the artifacts produced.

Alex E. Bell. Death by UML Fever. Queue 2(1), March 2004, ACM Press, 72 – 80, 2004

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Who would not be impressed ?

• Fig. 10: BRIDG Comprehensive Class and attribute diagram - (Logical diagram), p99

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I’m not !• I have come to appreciate domain modeling in

UML as an implementation-independent approach which is more likely to uncover “the truth” about the underlying semantics.– Dr. Diane Wold. Modeling Trial Design with BRIDG. July 26, 2006

• The UML diagram helped us to keep separate an activity, which exists independent of any schedule, and an activity-at-a-visit, (the X), which is a plan to perform that activity at a particular time.



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Rule 4:

Limit the number of developers/contributors

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Contributors to the BRIDG model

A chain is as

strong as its

weakest link

Image from: The BRIDG Project: Creating a model of the semantics of clinical trials research. Douglas B. Fridsma. July 26, 2006



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Rule 5:

• Be consistent in what you describe: – either representational units, or – the entities represented by them.

• Thus: keep the levels of reality all the time in mind

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LivingSubject (BRIDG logical model p1031)• Type: Class• Status: . Version . Phase .• Package: Entities and Roles Keywords:• Detail: Created on 02/09/2006. Last modified on

02/09/2006.• GUID: {7C04F8D8-30B9-4942-B2A8-4CF93E8913D9}• An object representing an organism or complex animal,

alive or not. Examples: person, dog, microorganism, plant of any taxonomic group, tissue sample, bacteria, fungi, and viruses.

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SubstanceAdministration (BRIDG logical model p84)

• Type: Class PerformedActivity• Status: Proposed. Version 1.0. Phase 1.0.• Package: CTOM Elements Keywords:• Detail: Created on 01/05/2005. Last modified on

12/14/2006.• GUID: {2289C0E8-855D-42e3-86FA-

2ECBE59D8982}• The description of applying, dispensing or giving

agents or medications to subjects.

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Person (BRIDG logical model p106 a.f., HE!)

• Type: Class• Status: Proposed. Version 1.0. Phase 1.0.• Package: Clinical Research Entities Keywords:• Detail: Created on 06/09/2005. Last modified on

01/13/2007.• GUID: {6F49F110-7B36-4c03-A7EA-

F456CE1E739D}• A human being.

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Some Person Attributes• administrativeGenderCode (p107)

– The classification of the sex or gender role of the patient. Values include: Female, Male, and Unknown.

• genderCode (p108)– The text that describes the assemblage of physical

properties or qualities by which male is distinguished from female; the physical difference between male and female within a person. [Explanatory Comment: Identification of sex is usually based upon self-report and may come from a form, questionnaire, interview, etc.]

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R T UA better example:

Clinical Trial Ontology under DOLCE

Crenguta Bogdan, Daniela Luzi, Fabrizio L. Ricci, Luca D. Serbanati. Towards a Clinical Trial Ontology using a Concern-Oriented Approach. W.P. n. 10, October 2006.



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Rule 6:

Use a Realism-based Upper Ontology to classify the representational units

in your Domain Ontology

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Realism in Basic Formal Ontology (BFO)• The world consists of

– entities that are • Either particulars or universals; • Either occurrents or continuants;• Either dependent or independent; and,

– relationships between these entities of the form• <particular , universal> e.g. is-instance-of, • <particular , particular> e.g. is-member-of• <universal , universal> e.g. isa (is-subtype-of)

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, November 8, 2006, Baltimore MD, USA

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Only what exists (or existed) can be represented• Anything else can be imagined• Examples of what exist:

– Body parts– Disorders– Abortions– Women with prevented abortions– Plans about my future activities

• What does not exist– Prevented abortions– My future activities

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PlannedActivity (BRIDG logical model p202, HE!)



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Rule 7:

• Use formal ontological methods to: – distinguish distinct entities– assess in what way distinct entities are

distinct

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Organism (BRIDG logical model p160, HE!)• Type: Class• Status: Proposed. Version 1.0. Phase 1.0.• Package: Clinical Research Roles Keywords:• Detail: Created on 12/13/2006. Last modified on

01/19/2007.• GUID: {B9F321DB-365F-4155-B8F6-3D….• The role that a biological entity has, and that role

participates in a microbiology test in two ways: first, it can be identified as the result of a microbiology test. It can also participate as a specimen in the microbiology test. [HL7 Perspective]

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An example: ONTOCLEAN

• Identity, essence, unity, dependence

C. Welty, N. Guarino"Supporting ontological analysis of taxonomic relationships", Data and Knowledge Engineering vol. 39, no. 1, pp. 51-74, 2001



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Rule 8:

• Don’t confuse reality with our means to access that reality, f.i.:

• Don’t confuse the observation of an entity with the entity observed

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AdverseEvent (BRIDG logical model p168, HE!)• Type: Class Assessment• Status: Proposed. Version 1.0. Phase 1.0.• Package: Clinical Research Activities Keywords:• Detail: Created on 05/24/2006. Last modified on

01/26/2007.• GUID: {CD620136-3CB9-4382-802B-F6CA82F98C10}• An observation of a change in the state of a subject that is

assessed as being untoward by one or more interested parties within the context of protocol-driven research or public health.

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Example: medical ‘findings’ and ‘observations’• A particular pathological entity may at a certain

time be undetectable by any observation method or technique available to an observer, including the person exhibiting the pathological entity itself.

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Example: medical ‘findings’ and ‘observations’ (1)• A particular pathological entity may at a certain


• A particular observation (‘act of looking’) may produce false results and thus simulate the existence of a pathological entity.

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Example: medical ‘findings’ and ‘observations’ (1)• A particular pathological entity may at a certain


• A particular observation may produce false results and thus simulate the existence of a pathological entity.

• An observer may observe or fail to observe a detectable particular pathological entity.

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On ‘findings’ and ‘observations’ (2)• When an observer perceives a particular pathological

entity, he might judge it – (1) to be an instance of the universal of which it is indeed an

instance in reality, – (2) to be an instance of another universal (and thus be in error),

or – (3) he might be not able to make an association with any

universal at all.• Distinct manifestations of ‘the same type’ may be

pathological or not:– Singing naked under the shower versus in front of The White

House• ...



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Rule 9:

Do not accept silly suggestions, whomever they come from

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Device (BRIDG logical model, p100, HE!)• Type: Class Material• Status: Proposed. Version 1.0. Phase 1.0.• Package: Clinical Research Entities Keywords:• Detail: Created on 02/22/2006. Last modified on

01/04/2007.• GUID: {3546A977-C51F-4860-A09A-

2ADAE896D74B}• <PROPOSED> A therapeutic or diagnostic intervention

utilizing a piece of equipment or a mechanism designed to serve a special purpose or perform a special function whose basic characteristics are not altered in the course of the intervention.

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The latter could also go under other rules:• Stop working when you are tired• Be careful with cut and paste• Proof-read your work• …



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Rule 10:

Use distinct names for distinct representational units that denote

distinct entities

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AdverseEvent (BRIDG logical model p504)• Type: Class HealthProblem• Status: Proposed. Version 1.0. Phase 1.0.• Package: Adverse Event Keywords:• Detail: Created on 05/01/2006. Last modified on

05/02/2006.• GUID: {6783F6F2-8837-4b7d-B81B-

A25206D36689}• A toxic reaction to a medical therapy, or to an

experience such as consuming a meal.

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AdverseEvent (BRIDG logical model p91)• Type: Class• Status: Proposed. Version 1.0. Phase 1.0.• Package: SDTM Keywords:• Detail: Created on 12/14/2005. Last modified on 12/28/2006.• GUID: {F1786F01-F973-426d-B765-0107B5823A18}• Any untoward medical occurrence in a patient or clinical

investigation subject administered a pharmaceutical product and which does not necessarily have a causal relationship with this treatment. An adverse event (AE) can therefore be any unintended sign (including an abnormal laboratory finding), symptom, or disease temporally associated with the use of a medicinal (investigational) product, whether or not related to the medicinal investigational) product.

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AdverseEvent (BRIDG logical model p36)• Type: Class Assessment• Status: Proposed. Version 1.0. Phase 1.0.• Package: CTOM (imported package) Keywords:• Detail: Created on 01/05/2005. Last modified on 09/26/2005.• GUID: {C0F30FE6-EE1E-443e-A7AB-256342B193B3}• An unfavorable and unintended reaction, symptom, syndrome, or

disease encountered by a subject while on a clinical trial regardless of whether or not it is considered related to the product or procedure. . The concept refers to assessments that could be medically related, dose related, route related, patient related, caused by an interaction with another therapy or procedure, or dose escalation.



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Rule 11:

Avoid contradictions

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ObjectiveResult (BRIDG logical model p191, HE!)• Type: Class InvestigativeResult, Observation• Status: Proposed. Version 1.0. Phase 1.0.• Package: Clinical Research Activities Keywords:• Detail: Created on 01/20/2005. Last modified on

12/28/2006.• GUID: {F388CFB0-77DE-4008-B222-EB…• An act of monitoring, recognizing and noting

reproducible measurement of some magnitude with suitable instruments or established scientific processes.

• <EXAMPLE> A laboratory test with standardized instruments, ECG measurement or question on a validated questionnaire such as SF36.

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Some attributes of ObjectiveResult• missedIndicator boolean

– This is an indicator flag that flags a performed observation as "not done". (default: CDISC) …… p193

• missedReason– This captures SDTM's ---REASND. In HL7, there is a list of

permissible missing value types, and we need to ensure that HL7's list is a superset of what is needed by SDTM.

– <EXAMPLE> A planned observation was not done because the equipment failed, so the corresponding "performed observation" exists as a placeholder to describe why that performed observation was not done…….……p193

– Default: [CDISC SDTM IG v3.1.1 = REASND ]



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Rule 12:

Avoid circular definitions

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Ingredient (BRIDG logical model p507) • Status: Proposed. Version 1.0. Phase 1.0.• Package: Adverse Event Keywords:• Detail: Created on 03/01/2006. Last modified on

03/01/2006.• GUID: {7D53B2A1-CEC4-49ae-8BD6-

611E2CF4D862}• A substance that acts as an ingredient within a

product. Note, that ingredients may also have ingredients.



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Rule 13:

Do not use names with a precise meaning in general language to designate entities which are of a more specific or totally

different type in the context of a specific application

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Animal (BRIDG logical model p526) • Type: Class InvestigatedParty• Status: Proposed. Version 1.0. Phase 1.0.• Package: InvestigatedSubject Keywords:• Detail: Created on 03/10/2006. Last modified on

03/10/2006.• GUID: {996CB91C-04EC-4b1d-9AFF-57B878D532D7}• A non-person living entity which is chosen to be the

subject of an investigation, or which is the subject of an• implicated act.



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Rule 14:

Provide a mechanism to let the ontology evolve in line with changes

in reality and in are understanding thereof

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Reality versus beliefs, both in evolution

IUI-#3

O-#2

O-#1

tU1

U2

p3Reality

BeliefO-#0

= “denotes” = what constitutes the meaning of representational units …. Therefore: O-#0 is meaningless

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Changes in reality, beliefs, representations

tU1

U2

p3

IUI-#3

O-#2

O-#1

R

BO-#0

Relationships amongst universals (R) or beliefs therein (B)

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Mistakes, discoveries, being lucky, having bad luck

tU1

U2

p3

IUI-#3

O-#2

O-#1

R

BO-#0

Mistakes

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tU1

U2

p3

IUI-#3

O-#2

O-#1

R

BO-#0

discoveries

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tU1

U2

p3

IUI-#3

O-#2

O-#1

R

BO-#0

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tU1

U2

p3

IUI-#3

O-#2

O-#1

R

BO-#0

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Key requirement for versioning

Any change in an ontology or data repository should be

associated with the reason for that change to be able to assess later what kind of mistake has been made !

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Example: a person’s gender in the EHR• In John Smith’s EHR:

– At t1: “male” at t2: “female”

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• What are the possibilities ?

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• What are the possibilities ?• Change in reality:

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• transgender surgery

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• transgender surgery• change in legal self-identification

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• Change in understanding: it was female from the very beginning but interpreted wrongly

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• Change in understanding: it was female from the very beginning but interpreted wrongly

• Correction of data entry mistake: it was understood as male, but wrongly transcribed

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Conclusion• Building high quality ontologies is hard.• Experts in driving cars are not necessarily experts in car

mechanics (and the other way round). – Good computer scientists are usually lousy ontologists

• Ontologies should represent the state of the art in a domain, i.e. the science. – Science is not a matter of consensus or democracy.

• Natural language relates more to how humans talk about reality or perceive it, than to how reality is structured.

• No high quality ontology without the involvement of ontologists.

Documents

Werner CEUSTERS, MD Center of Excellence in Bioinformatics and Life Sciences