The Foundations of Biomedical
Ontology
Barry Smith
http://ontology.buffalo.edu/smith
1
Ontology (Phil.)
= the science of the types of objects,
qualities, proesses, events, funktions,
environments, relations ... in all spheres of
reality
2
Google hits (in millions) 12.10.06
ontology
24.0
ontology + philosophy
4.6
ontology + information science 7.4
ontology + database
11.1
3
4
ontology (computer science)
(roughly) the construction of
standardized classification
systems designed to support
compatibility and integration of
data
5
National Center for
Biomedical Ontology
$18.8 mill. NIH Roadmap Center
•
Stanford Medical Informatics
•
University of San Francisco Medical Center
•
Berkeley Drosophila Genome Project
•
Cambridge University Department of Genetics
•
The Mayo Clinic
•
University at Buffalo Department of Philosophy
6
From
chromosome
to disease
genomics
transcriptomics
proteomics
reactomics
metabonomics
phenomics
behavioromics
connectomics
toxicopharmacogenomics
bibliomics
… legacy of Human Genome Project
8
where in the body ?
what kind of
disease process ?
 need for semantic annotation of data
10
how create broad-coverage semantic
annotation systems for biomedicine?
covering:
in vitro biological phenomena
model organisms
humans
11
12
13
Two types of ontology
natural-science ontologies capture
terminology-level knowledge used by best
current science
vs.
administrative ontologies (e.g. billing
ontologies, bloodbank ontologies, lab
workflow ontologies)
14
Mission of the NCBO
To create software and support services for
science-based ontology development and
use in the biomedical domain
Science-based = ontologies for support of
scientific research (taken as encompassing
evidence-based medicine)
Science-based = using the scientific method
as part of the process of ontology
development and testing
15
Scientific ontologies have special
features
Every term in a scientific ontology must be
such that the developers of the ontology
believe it to refer to some entity on the basis
of the best current evidence
16
For scientific ontologies
reusability is crucial
compatibility with neighboring scientific
ontologies is crucial  it should not be too
easy to add new terms to an ontology
we want to introduce these features in clinical
medicine ...
17
For scientific ontologies
the issue of how the ontology will be used is
not a factor relevant for determining which
entities will be acknowledged by the
ontology
If this decision is made on specific practical
needs, this will thwart reusability of the data
the ontology is used to annotate
18
Administrative ontologies
Entities may be brought into existence by the
ontology itself. (Convention ...)
Highly task-dependent – reusability and
compatibility not (always) important
Developers may invent dummy entities (‘surgical
procedure not performed because of patient
request’) e.g. for forensic reasons (reality and
knowledge are confused)
19
Hypothesis
Many of the shortfalls of existing
administrative ontologies can be overcome
by adopting the scientific approach
A good theory is, in the long run, also
practically useful
Administrative ontologies ~ data models
20
An Ontological Square
Ontologies in
support of
science
Administrative
ontologies
An Ontological Square
Upper-level
integrating
ontologies
Domain
ontologies
22
An Ontological Square
Upper-level
integrating
ontologies
Domain
ontologies
Ontologies in
support of
science
Administrative
ontologies
23
An Ontological Square
Upper-level
integrating
ontologies
Domain
ontologies
Ontologies in
support of
science
BFO (Basic Formal SNOMED
Ontology)
SwissProt
DOLCE
FMA
Administrative
ontologies
(for ecommerce, etc.)
FOAF top level:
person, topic,
document, primary
topic ...
Amazon.com
ontology
Library of
Congress Catalog
24
Problem of ensuring sensible
cooperation in a massively
interdisciplinary community
concept
type
instance
model
representation
data
25
Entity =def
anything which exists, including things and
processes, functions and qualities, beliefs
and actions, documents and software
(Levels 1, 2 and 3)
28
what are the kinds of entity?
29
First basic distinction
universal vs. instance
(science text vs. diary)
(human being vs. Tom Cruise)
30
For science, and thus for
scientific ontologies,
it is generalizations that are
important = universals, types,
kinds, species
31
Catalog vs. inventory
A
B
C
515287
521683
521682
DC3300 Dust Collector Fan
Gilmer Belt
Motor Drive Belt
32
Catalog vs. inventory
33
Catalog of Universals/Types
Ontology
Universals
Instances
35
Ontology = A Representation of
Universals
36
Each node of an ontology
consists of:
• preferred term (aka term)
• term identifier (TUI, aka CUI)
• synonyms
• definition, glosses, comments
Ontology = A representation of
universals
37
An ontology is a representation
of universals
We learn about universals in reality from
looking at the results of scientific
experiments in the form of scientific theories
experiments relate to what is particular
science describes what is general
38
universals
substance
organism
animal
mammal
cat
siamese
instances
frog
Domain =def
a portion of reality that forms the subjectmatter of a single science or technology or
mode of study or administrative practice ...;
proteomics
HIV
epidemiology
40
Representation =def
an image, idea, map, picture, name or
description ... of some entity or entities.
41
Ontologies are representational
artifacts
comparable to science texts
and subject to the same sorts
of constraints (including
need for update)
42
Representational units =def
terms, icons, alphanumeric identifiers ...
which refer, or are intended to refer, to
entities
and which are minimal (atoms)
43
The Periodic Table
Periodic Table
46
Ontologies are here
47
or here
48
ontologies represent general
structures in reality (leg)
49
Ontologies do not represent
concepts in people’s heads
50
They represent universals in reality
51
“leg” is not the name of a concept
concepts do not stand in the
part_of
connectedness
causes
treats ...
relations used by biomedical ontologies
52
instances
A
B
C
515287
521683
521682
DC3300 Dust Collector Fan
Gilmer Belt
Motor Drive Belt
universals
Inventory vs. Catalog:
Two kinds of representational
artifact
Databases represent instances
Ontologies represent universals
54
How do we know which general
terms designate universals?
Roughly: terms used by scientists to
designate entities about which we have a
plurality of different kinds of testable
proposition
(cell, electron ...)
55
Language has the power to create
general terms
which go beyond the domain of universals
studied by science
56
Problem: fiat demarcations
male over 30 years of age with family history
of diabetes
abnormal curvature of spine
participant in trial #2030
57
Problem: roles
fist
patient
FDA-approved drug
58
Administrative ontologies often need
to go beyond universals
Fall on stairs or ladders in water transport injuring
occupant of small boat, unpowered
Railway accident involving collision with rolling
stock and injuring pedal cyclist
Nontraffic accident involving motor-driven snow
vehicle injuring pedestrian
59
Class =def
a maximal collection of particulars
determined by a general term
(‘cell’. ‘electron’ but also: ‘ ‘restaurant in
Palo Alto’, ‘Italian’)
the class A
= the collection of all particulars x for
which ‘x is A’ is true
60
universals vs. their extensions
universals
{a,b,c,...}
collections of particulars
61
Extension =def
The extension of a universal A is the class:
instance of the universal A
(it is the class of A’s instances)
(the class of all entities to which the term ‘A’
applies)
62
Problem
The same general term can be used to refer
both to universals and to collections of
particulars. Consider:
HIV is an infectious retrovirus
HIV is spreading very rapidly through Asia
63
universals vs. classes
universals
{c,d,e,...}
classes
64
universals vs. classes
universals
defined classes
65
universals vs. classes
universals
populations, ...
66
Defined class =def
a class defined by a general term which
does not designate a universal
the class of all diabetic patients in
Leipzig on 4 June 1952
67
OWL is a good representation of
defined classes
• sibling of Finnish spy
• member of Abba aged > 50 years
68
Terminology =def.
a representational artifact whose
representational units are natural language
terms (with IDs, synonyms, comments, etc.)
which are intended to designate universals
together with defined classes.
69
universals, classes, concepts
universals
defined classes
‘concepts’
?
70
universals < defined classes <
‘concepts’
‘concepts’ which do not correspond to
defined classes:
‘Surgical or other procedure not carried out
because of patient's decision’
‘Congenital absent nipple’
because they do not correspond to anything
71
(Scientific) Ontology =def.
a representational artifact whose representational
units (which may be drawn from a natural or from
some formalized language) are intended to
represent
1. universals in reality
2. those relations between these universals which
obtain universally (= for all instances)
lung is_a anatomical structure
lobe of lung part_of lung
72
Part II: How to Build an Ontology
73
How to build an ontology
work with scientists to create an initial top-level
classification
find ~50 most commonly used terms corresponding
to universals in reality
arrange these terms into an informal is_a hierarchy
according to this Universality principle
A is_a B  every instance of A is an instance of B
fill in missing terms to give a complete hierarchy
(leave it to domain scientists to populate the lower
levels of the hierarchy)
74
Principle of Low Hanging Fruit
Include even absolutely trivial assertions
(assertions you know to be universally true)
pneumococcal virus is_a virus
Computers need to be led by the hand
75
MeSH
MeSH Descriptors
Index Medicus Descriptor
Anthropology, Education, Sociology and
Social Phenomena (MeSH Category)
Social Sciences
Political Systems
National Socialism
National Socialism is_a Political Systems
National Socialism is_a Anthropology ...
76
Principle
Use singular nouns
Terms in ontologies represent universals
77
Goal: Each term in an ontology
represents exactly one universal
there are universals also of
collectivities:
population
complex of cells
78
the use-mention confusion
Conceptual Entities =Def.
An organizational header for concepts
representing mostly abstract entities.
swimming is healthy and has eight letters
79
Principle
Avoid confusing between words and things
Avoid confusing between concepts in our
minds and entities in reality
Recommendation: avoid the word ‘concept’
entirely
80
Trialbank
‘information’ = def. ‘a written or spoken
designation of a concept’
81
Trialbank
‘Heparin therapy’ is an instance of ‘written or
spoken designation of a concept’
What are the problems here?
1. misuse of quotation marks
2. confusion of instances and universals
3. confusion of concept and reality
82
Plant Ontology
cell = def. plant cell, consisting of protoplast
and cell wall; ...
what happens when the users of the Plant
Ontology need to consider bacterial
pathogens in plants?
83
Principle
For the sake of interoperability with other
ontologies, do not give special meanings to
terms with established general meanings
(Don’t use ‘cell’ when you mean ‘plant cell’)
84
ICNP: International Classification of
Nursing Procedures
water =def. a type of Nursing Phenomenon
of Physical Environment with the specific
characteristics: clear liquid compound of
hydrogen and oxygen that is essential for
most plant and animal life influencing life
and development of human beings.
85
Principle
Supply definitions wherever possible
(both human-understandable natural
language definitions, and equivalent formal
definitions)
86
Principle
Each term should have at most one definition
which may have both natural-language
and formal versions
87
The Problem of Circularity
A Person = def. A person with an identity
document
cell = def. plant cell, consisting of protoplast
and cell wall; ...
88
Principle
Avoid circular definitions
(The term defined should not appear in its
own definition)
89
HL7
‘stopping a medication’ = def.
change of state in the record of a
Substance Administration Act from
Active to Aborted
90
Principle
A definition should use terms which are
easier to understand than the term defined
(HL7 creates a topsy turvy world, in which
simple things are made difficult)
91
Principle
Use Aristotelian definitions
An A is a B which C’s.
A human being is an animal which is rational
92
Principle
Do not seek to define everything
93
In every ontology
some terms and some relations are
primitive = they cannot be defined (on
pain of infinite regress)
Examples of primitive relations:
identity
instance_of
94
Principle
(a good, general constraint on a
theory of meaning)
For each linguistic expression ‘E’
‘E’ means E
‘snow’ means: snow
‘pneumonia’ means: pneumonia
95
HL7 Reference Information Model
‘medication’ does not mean: medication
rather it means:
the record of medication in an information
system
‘disease does not mean: disease
rather it means:
the observation of a disease
96
Univocity
Terms should have the same meanings on
every occasion of use.
(= They should refer to the same universals)
Basic ontological relations such as is_a and
part_of should be used in the same way
by all ontologies
98
Universality
Ontologies are made of relational
assertions
They should include only those which hold
universally
pneumococcal virus causes pneumonia
99
Universality
Often, order will matter:
We can assert
adult transformation_of child
but not
child transforms_into adult
100
Universality
viral pneumonia caused by virus
but not
virus causes pneumonia
pneumococcal virus causes pneumonia
101
Universality
protocol-design earlier_than results analysis
but not
results analysis later_than protocol-design
102
Positivity
Complements of universals are not
themselves universals.
Terms such as
non-mammal
non-membrane
other metalworker in New Zealand
do not designate universals in reality
103
Ontology of universals  logic of terms
There are no conjunctive and disjunctive
universals:
anatomic structure, system, or substance
musculoskeletal and connective tissue
disorder
rheumatism, excluding the back
104
Objectivity
Which universals exist in reality is not a
function of our knowledge.
Terms such as
unknown
unclassified
unlocalized
arthropathies not otherwise specified
do not designate universals in reality.
105
Keep Epistemology Separate from
Ontology
If you want to say that
We do not know where A’s are located
do not invent a new class of
A’s with unknown locations
(A well-constructed ontology should grow
linearly; it should not need to delete classes
or relations because of increases in
knowledge)
106
Keep Sentences Separate from
Terms
If you want to say
I surmise that this is a case of pneumonia
do not invent a new class of surmised
pneumonias
Confusion of ‘findings’ in medical terminologies
107
Single Inheritance
No kind in a classificatory hierarchy
should have more than one is_a
parent on the immediate higher
level
108
Multiple Inheritance
thing
car
blue thing
is_a
is_a
blue car
109
Multiple Inheritance
is a source of errors
encourages laziness
serves as obstacle to integration with
neighboring ontologies
hampers use of Aristotelian methodology for
defining terms
hampers use of statistical search tools
110
Multiple Inheritance
thing
blue thing
car
is_a1
is_a2
blue car
111
is_a Overloading
The success of ontology alignment
demands that ontological relations (is_a,
part_of, ...) have the same meanings in the
different ontologies to be aligned.
112
Multiple Inheritance
thing
blue thing
car
is_a1
is_a2
blue car
113
How to solve this problem
Create two ontologies:
of cars
of colors
Link the two together via cross-products
(= factoring, normalization, modularization)
114
Compositionality
The meanings of compound terms should be
determined
1. by the meanings of component terms
together with
2. the rules governing syntax
115
Why do we need rules/standards for
good ontology?
Ontologies must be intelligible both to humans (for
annotation and curation) and to machines (for
reasoning and error-checking): the lack of rules
for classification leads to human error and blocks
automatic reasoning and error-checking
Intuitive rules facilitate training of curators and
annotators
Common rules allow alignment with other ontologies
116
think of ontologies as legends for
cartoons
cartoons, like maps, always have a
certain threshold of granularity
but they can be veridical representations of
reality nonetheless
Goal: use logically well-structured ontologies
to create algorithmic, dynamic cartoons
118
Randomized controlled trials
http://rctbank.ucsf.edu/ontology/outline/index.htm
119
Top-Level Class Hierarchy for
RCT
Root
Secondary-study
Trial-details
Trial
Concept
•
•
•
•
•
•
•
Generic-concept
Population-concept
Protocol-concept
Design-concept
Outcome-concept
Administrative-concept
Intervention-concept
120
Trial Details
Root
Secondary-study
Trial-details
•
•
•
•
Erratum
Publication-details
Trial-entry-details
Administrative-details
– Secondary-administrative-details
– Primary-administrative-details
» Executed-administrative-details
» Intended-administrative-details
• Conclusion-details
• Background-details
– Intended-background-details
– Executed-background-details
•
•
•
•
Stopping-details
Retraction-details
Correction-details
Fraud-details
121
Top-Most Class Hierarchy for RCT
Root
Secondary-study
Trial-details
Trial
Concept
•
•
•
•
•
•
•
Generic-concept
Population-concept
Protocol-concept
Design-concept
Outcome-concept
Administrative-concept
Intervention-concept
122
Concept
• Generic-concept
–
–
–
–
Term-information
Time-entity
Rule-concept
Situation
• Population-concept
–
–
–
–
–
Subgroup
Recruitment-flowchart
Population
Recruitment
Site-enrollment
• Protocol-concept
–
–
–
–
–
–
–
–
–
Follow-up-compliance
Follow-up-activity
Follow-up
Protocol-change
Treatment-assignment
Protocol
Reason
Outcomes-followup
Secondary-study-protocol
123
Concept
• Design-concept
–
–
–
–
–
–
–
–
–
Survival-analysis-and-results
Statistical-analysis-and-results
Sample-size-calculation
Trial-design
Hypothesis-concept
Study-objective
Study-monitoring
Regression-analysis-and-results
Stopping-rule
• Outcome-concept
–
–
–
–
–
–
Special-variable-information
Outcome-assessment
Miscellaneous-outcome-entity
Result-entity
Outcome-value-entity
Outcome
124
Concept
• Administrative-concept
–
–
–
–
–
–
–
–
Publication-concept
Study-site
Person
Ethics
Study-committee
Funder
Institution
Registry-id
• Intervention-concept
–
–
–
–
–
–
–
–
Blinding-concept
Compliance-details
Intervention-step
Intervention-arm
Co-intervention
Intervention
Compliance-result
Intervention-logic
125
Top-Level Class Hierarchy for
RCT
Root
Secondary-study
Trial-details
Trial
Concept
•
•
•
•
•
•
•
Generic-concept
Population-concept
Protocol-concept
Design-concept
Outcome-concept
Administrative-concept
Intervention-concept
126
Basic Formal Ontology
What the top level should look like
127
Two kinds of entities
occurrents (processes, events, happenings)
continuants (objects, qualities, states...)
128
Continuants (aka endurants)
have continuous existence in time
preserve their identity through change
exist in toto whenever they exist at all
Occurrents (aka processes)
have temporal parts
unfold themselves in successive phases
exist only in their phases
129
You are a continuant
Your life is an occurrent
You are 3-dimensional
Your life is 4-dimensional
130
Dependent entities
require independent continuants as their
bearers
There is no run without a runner
There is no grin without a cat
131
Dependent vs. independent
continuants
Independent continuants (organisms,
buildings, environments)
Dependent continuants (quality, shape,
role, propensity, function, status, power,
right)
132
All occurrents are dependent entities
They are dependent on those independent
continuants which are their participants
(agents, patients, media ...)
133
BFO Top-Level Ontology
Continuant
Independent
Continuant
Occurrent
(always dependent
on one or more
independent
continuants)
Dependent
Continuant
134
= A representation of top-level types
Continuant
Occurrent
biological process
Independent
Continuant
Dependent
Continuant
cell component
molecular function
135
Top-Level Ontology
Continuant
Independent
Continuant
Occurrent
Dependent
Continuant
Function
Side-Effect,
Stochastic
Process, ...
Functioning
136
Top-Level Ontology
Continuant
Independent
Continuant
Dependent
Continuant
Occurrent
Functioning
Side-Effect,
Stochastic
Process, ...
Function
137
Top-Level Ontology
Continuant
Independent
Continuant
Quality
Dependent
Continuant
Function
Occurrent
Functioning
Side-Effect,
Stochastic
Process, ...
Spatial
Region
instances (in space and time)
138
Towards a Clinical Trial Ontology
To serve merger of data schemas
To serve flexibility of collaborative clinical trial
research
To serve management of clinical trial research
To serve data access and reuse
141
CTO will be part of OBI
Ontology of Biomedical Investigations
http://obi.sourceforge.net
which is in turn part of the OBO Foundry
http://obofoundry.org
142
Overview of the Ontology of
Biomedical Investigations
with thanks to Trish Whetzel on
behalf of the FuGO Working Group
143
OBI
Purpose
Provide a resource for the unambiguous description of the
components of biomedical investigations such as the
design, protocols and instrumentation, material, data and
types of analysis on the data
 NOT designed to model biology
Enables
Allow consistent annotation of data across different
technological and biological domains
Enable powerful queries
Facilitate semantically-driven data integration
144
Motivation for OBI
Standardization efforts in biological and
technological domains
Standard syntax - Data exchange formats
 To provide a mechanism for software
interoperability, e.g. FuGE Object Model
Standard semantics - Controlled
vocabularies or ontology
 Centralize commonalities for annotation term
needs across domains to describe an
investigation/study/experiment, e.g. FuGO
145
Biomedical Investigation
Components
Investigation Design
Material and It's Characteristics
Treatments
Sample Analysis Preparation
Instrumental Analysis
Data Pre-Processing
Computational/Higher Level Analysis
Describe the design and purpose or general
aim of the the Investigation.
Describe the material and characteristics.
Describe the manipulations or perturbations or
observations performed on the material to
meet the general aim of the investigation.
Describe how the material was prepared for
analysis - e.g. labeling, protein digest, etc.
Describe the instrument and settings that
were used.
Describe the results from the instrument,
e.g. what units are represented.
Describe the type analysis performed to
confirm/deny the hypothesis, e.g.
146
clustering.
FuGO Development
Strategy Decisions
Unified Development
Pros
Pros

Overlap of terms is identified
early in development

Universal/Common terms are
defined by all those
collaborating

Additional technological or
biological terms can be
added as needed by
collaborators
Cons

Independent Development
Time needed to develop the
ontology
 Develop ‘Ontology’ in a time
frame limited only by the
community
Cons
 Development of different
working policies?
 Use of different top level
classes?
 Overlap of terms at lower
levels of the ontology tree
147
FuGO Development Process
Collect Use Cases - within community activity
Collect examples of investigations as performed within a community
and present Use Cases to developers group
Bottom up approach - within community activity
Identify concepts to describe using controlled terms
Collect terms and their definitions
Bin terms in the top level ontology structure
Top down approach - collaborative activity
Build a top level ontology structure, is_a (vertical) relationships
Make a list of other foreseen (horizontal) relationships
Review how Top Level Nodes fit in with the Upper Level Ontologies
148
FuGO - Top Level Classes
Continuant: an entity that endure/remains the same through time
Dependent Continuant: depend on another entity
E.g. Environment (depend on the set of ranges of conditions, e.g. geographic location)
E.g. Characteristics (entity that can be measured, e.g. temperature, unit)
- Realizable: an entity that is realizable through a process (executed/run)
E.g. Software (a set of machine instructions)
E.g. Design (the plan that can be realized in a process)
E.g. Role (the part played by an entity within the context of a process)
Independent Continuant: stands on its own
E.g. All physical entity (instrument, technology platform, document etc.)
E.g. Biological material (organism, population etc.)
Occurrent: an entity that occurs/unfold in time
E.g. Temporal Regions, Spatio-Temporal Regions (single actions or Event)
Process
E.g. Investigation (the entire ‘experimental’ process)
E.g. Study (process of acquiring and treating the biological material)
E.g. Assay (process of performing some tests and recording the results)
149
Emerging FuGO Design Principles
OBO Foundry ontology, utilize ontology best practices
Inherit top level classes from an Upper Level ontology
Use of the Relation Ontology
Follow additional OBO Foundry principles
Facilitates interoperability with other OBO Foundry ontologies
Develop recommendations for naming conventions and metadata
Format for term names, e.g. underscore vs. camel case, no purals
Use of Alphanumeric identifier for terms, I.e. something that does not have semantic
meaning
Mechanisms for adding synonyms, etc.
Open source approach
Protégé/OWL
Weekly conference calls
Shared environment using Sourceforge (SF) and SF mailing lists
150
Future Plans
Binning process - ongoing
Reconciliations into one canonical version
Iterative process
Common working practices - established
Each class consists of: unique alphanumeric
identifier, human readable string name, definition
and comments
Sourceforge tracker in place to collect comments on
terms, definitions, relationships
Review ontology so that top level classes meet the
needs of all involved ‘communities’
151
OBI Collaborating Communities
Crop sciences Generation Challenge Programme (GCP), www.generationcp.org
Environmental genomics MGED RSBI Group, www.mged.org/Workgroups/rsbi
Genomic Standards Consortium (GSC), www.genomics.ceh.ac.uk/genomecatalogue
HUPO Proteomics Standards Initiative (PSI), psidev.sourceforge.net
Immunology Database and Analysis Portal, www.immport.org
Immune Epitope Database and Analysis Resource (IEDB),
http://www.immuneepitope.org/home.do
International Society for Analytical Cytology, http://www.isac-net.org/
Metabolomics Standards Initiative (MSI), msi.workgroups.sourceforge.net
Neurogenetics, Biomedical Informatics Research Network (BIRN), www.nbirn.net
Nutrigenomics MGED RSBI Group, www.mged.org/Workgroups/rsbi
Polymorphism
Toxicogenomics MGED RSBI Group, www.mged.org/Workgroups/rsbi
Transcriptomics MGED Ontology Group, mged.sourceforge.net/ontologies
152
http://fugo.sourceforge.net
153
http://obi.sourceforge.net
154
155
156
157
158
159
160
161
Top-Level Class Hierarchy for
RCT
Root
Secondary-study
Trial-details
Trial
Concept
•
•
•
•
•
•
•
Generic-concept
Population-concept
Protocol-concept
Design-concept
Outcome-concept
Administrative-concept
Intervention-concept
162
Amended Top-Level Class
Hierarchy for RCT
Entity
Continuant
Population
Protocol
Design
Occurrent
Trial
Secondary-study
Intervention
?? Trial-details
?? Outcome-concept
?? Administrative-concept
163
Concept
• Generic-concept
– Term-information
– Time-entity
– Rule-concept
» Clinical-rule
Exclusion-rule
Inclusion-rule
» Rule-entity
Recursive-rule
Base-rule
» Ethnicity-language-rule
» Age-gender-rule
» Situation
164
165
166
Concept
• Protocol-concept
–
–
–
–
–
–
–
–
–
Follow-up-compliance
Follow-up-activity
Follow-up
Protocol-change
Treatment-assignment
Protocol
Reason
Outcomes-followup
Secondary-study-protocol
167
Amended Top-Level Class
Hierarchy for RCT
Entity
Continuant
Protocol
• Secondary-study-protocol
Reason
Occurrent
• Treatment-assignment
• Follow-up
– Follow-up-activity
– Outcomes-follow-up
• Protocol-change
168
Concept
• Population-concept
–
–
–
–
–
Subgroup
Recruitment-flowchart
Population
Recruitment
Site-enrollment
169
Amended Top-Level Class
Hierarchy for RCT
Entity
Continuant
Protocol
• Secondary-study-protocol
Recruitment-flowchart
Reason
Population
• Subgroup
Occurrent
• Priors
– Recruitment
– Site-enrollment
– Treatment-assignment
• Follow-up
– Follow-up-activity
– Outcomes-follow-up
• Protocol-change
170
Concept
• Administrative-concept
–
–
–
–
–
–
–
–
Publication-concept
Study-site
Person
Ethics
Study-committee
Funder
Institution
Registry-ID
171
Continuant
• Information object
– Publication
– Registry-ID
• Study-site
• Person
• Institution
– Study-committee
– Funder
???Ethics
172
Concept
• Intervention-concept
–
–
–
–
–
–
–
–
Blinding-concept
Compliance-details
Intervention-step
Intervention-arm
Co-intervention
Intervention
Compliance-result
Intervention-logic
173
Occurrent
• Intervention
–
–
–
–
Blinding
Intervention-step
Intervention-arm
Co-intervention
• ??? Intervention-logic
• ??? Compliance-result
• ??? Compliance-details
174
175
178