1. Center of Excellence in Bioinformatics and Life Sciences
GCRC SEMINAR Referent Tracking for Clinical and Translational Research Burlington, VT, USA, June 16, 2008
Werner CEUSTERS, MD
Center of Excellence in Bioinformatics and Life Sciences

2. Short personal history
1977
2006
Short personal history
2004
1989
1992
2002
1995
1998
1993

3. ‘The spectrum of the Health Sciences’
Turning data in knowledge ?

4. Source of all data
Reality !

5. Ultimate goal of Referent Tracking
A digital copy of the world

6. Requirements for this digital copy
R1: A faithful representation of reality
R2 … of everything that is digitally registered,
what is generic  scientific theories
what is specific  what individual entities exist and how they relate
R3: … throughout reality’s entire history,
R4 … which is computable in order to …
… allow queries over the world’s past and present,
… make predictions,
… fill in gaps,
… identify mistakes,
...

7. In fact … the ultimate crystal ball

8. The ‘binding’ wall
How to do it right ?
A cartoon of the world

9. Structure of this presentation
Problem statement:
Today’s Health IT infrastructure is highly capable to communicate data and knowledge, but lacks reliable methods:
a) to assess whether the information is any good, and
b) to integrate data and knowledge.
Proposed solutions:
Realism-based ontology
Referent Tracking
Applications and projects.

10. Where is health IT now ?

11. Better information care
A general belief:
Better
information
care

12. ‘Information’ versus ‘informing’
Being better informed
Better
information
care

13. Being better informed Better Better information care
A general belief:
Being better informed
Concerns primarily the delivery of information:
Being better informed
Better
Better
information
care

14. A general belief: Being better informed pretty well covered
Concerns primarily the delivery of information:
Timely,
Where required (e.g. bed-side computing),
What is permitted,
What is needed.
Involves:
Connecting systems,
Making systems interoperable:
Syntactically,
Semantically.
pretty
well
covered
long way to go

15. “Better Information” must cover …
Patient-specific information
EHR-EMR-ENR-…
PHR
Various modality related databases
Lab, imaging, …
Textbooks
Classification systems
Terminologies
Ontologies 1 3
Scientific “knowledge” 2

16. Key questions …
What does ‘better’ information mean ?
Is the current quality of health information not good enough ?
If not, how to make it better ?

17. 2
NCI Thesaurus (April 2008)

18. 2
NCI Thesaurus (April 2008) ?

19. Diabetes Mellitus in MeSH 2008?
Different set of more specific terms when different path from the top is taken.

20. Snomed CT (July 2007): “fractured nasal bones”

21. Cause: coding / classification confusion2
Cause: coding / classification confusion
A patient with a fractured nasal bone
A patient with a broken nose
A patient with a fracture of the nose =

22. Cause: coding / classification confusion2
Cause: coding / classification confusion
A patient with a fractured nasal bone
A patient with a broken nose
A patient with a fracture of the nose =
A patient with a fractured nasal bone
A patient with a broken nose
A patient with a fracture of the nose =

23. MeSH: some paths from top to Wolfram Syndrome2
Wolfram Syndrome
All MeSH Categories
Diseases Category
Nervous System Diseases
Cranial Nerve
Diseases
Optic Nerve
Optic Atrophy
Optic Atrophies,
Hereditary
Eye Diseases
Eye Diseases,
Hereditary
Optic Nerve
Diseases
Male Urogenital
Diseases
Urologic Diseases
Kidney Diseases
Diabetes Insipidus
Female Urogenital Diseases
and Pregnancy Complications
Neurodegenerative
Diseases
Heredodegenerative
Disorders,
Nervous System

24. What would it mean if used in the context of a patient ?
??? 3
Wolfram Syndrome
All MeSH Categories
Diseases Category
Nervous System Diseases
Cranial Nerve
Diseases
Optic Nerve
Optic Atrophy
Optic Atrophies,
Hereditary
???
Eye Diseases
Eye Diseases,
Hereditary
Optic Nerve
Diseases
Female Urogenital Diseases
and Pregnancy Complications
Male Urogenital
Diseases
Urologic Diseases
Kidney Diseases
Diabetes Insipidus
Neurodegenerative
Diseases
Heredodegenerative
Disorders,
Nervous System …
has
has

25. How many disorders are listed ?
The same type of location code used
in relation to three different events might or might not refer to the same
location. 1
How many disorders are listed ?
5572
04/07/1990
closed fracture of shaft of femur
Fracture, closed, spiral
12/07/1990
Accident in public building (supermarket)
79001
Essential hypertension
0939
24/12/1991
benign polyp of biliary tract
2309
21/03/1992
47804
03/04/1993
Other lesion on other specified region
17/05/1993
298
22/08/1993
Closed fracture of radial head
01/04/1997
PtID
Date
ObsCode
Narrative
20/12/1998
malignant polyp of biliary tract
Three references of hypertension for the same
patient denote three times the same disease.
If the same fracture code is used for the same patient on different dates, then these codes might or might not refer to the same fracture.
If two different fracture codes are used in relation to observations made on the same day for the same patient, they might refer to the same fracture
If two different tumor codes are used
in relation to observations made on different
dates for the same patient,
they may still refer to the same tumor.
The same fracture code used in relation
to two different patients can not refer to
the same fracure.

26. Realism-based Ontology
Major problems
Solutions
A mismatch between what is - and has been - the case in reality, and representations thereof in:
(generic) Knowledge repositories, and
(specific) Data and Information repositories.
An inadequate integration of a) and b). P h i l o s p y H I T
Philosophical
realism
Realism-based Ontology
Referent Tracking

27. Requirements for a digital copy of the world
R1: A faithful representation of reality
R2 … of everything that is digitally registered,
what is generic  scientific theories
what is specific  what individual entities exist and how they relate
R3: … throughout reality’s entire history,
R4 … which is computable in order to …
… allow queries over the world’s past and present,
… make predictions,
… fill in gaps,
… identify mistakes,
...

28. Realism-based Ontology

29. Get down that wall
Realism-based Ontology: teaches us how to build an adequate grid.
Granular Partition Theory: relates the copy to reality.

30. ‘Ontology’: one word, two meanings
In philosophy:
Ontology (no plural) is the study of what entities exist and how they relate to each other;
In computer science and (biomedical informatics) applications:
An ontology (plural: ontologies) is a shared and agreed upon conceptualization of a domain;
Our ‘realist’ view within the Ontology Research Group combines the two:
We use realism, a specific theory of ontology, as the basis for building high quality ontologies, using reality as benchmark.

31. Realism-based ontology
Basic assumptions:
reality exists objectively in itself, i.e. independent of the perceptions or beliefs of cognitive beings;
reality, including its structure, is accessible to us, and can be discovered through (scientific) research;
the quality of an ontology is at least determined by the accuracy with which its structure mimics the pre-existing structure of reality.

32. Three major views on reality
Realism
Conceptualism
Nominalism
Basic questions:
What does a general term such as ‘diabetes’ refer to?
Do generic things exist?
Universal
Concept
Collection of
particulars
yes: in
particulars
perhaps: in
minds no

33. Dominant view in computer science is conceptualism
Realism
Conceptualism
Nominalism
Basic questions:
What does a general term such as ‘tree’ refer to?
Do generic things exist?
yes: in
particulars
perhaps: in
minds no
Universal
Concept
Collection of

34. Dominant view in computer science is conceptualism
Realism
Conceptualism
Nominalism
Semantic
Triangle
concept
object
term
Embedded in
Terminology

35. ‘Terminology’: one word, two meanings
Terminology is the study of identifying and labelling ‘concepts’ pertaining to a subject field.
Terminology related activities:
analysing the concepts and concept structures,
identifying the terms assigned to the concepts,
establishing correspondences between terms, possibly in various languages,
compiling a terminology, on paper or in databases,
managing terminology databases,
creating new terms, as required.

36. Why this interest in biomedical terminologies?
‘Nuances in the English language can be both challenging and amusing, however, when variants in language impact treatment, safety and billing, it is all challenge and no humor.
Although English contains a reasonable degree of conformity, divergence in phrasing and meaning can compound comprehension problems and impact patient safety. These language "woes" can be minimized through the use of sophisticated healthcare IT systems with terminology management services.’
Schwend GT. The language of healthcare. Variance in the English language is harming patients and hospitals' bottom lines. Is healthcare IT the solution? Health Manag Technol Feb;29(2):14, 16, 18

37. This is the ‘terminology / ontology divide’
However …
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
This is the ‘terminology / ontology divide’

38. The semantic triangle revisited
Representation and Reference
First Order Reality
concepts
about
terms
concepts
objects
terms

39. Terminology Realist Ontology
Representation and Reference
concepts
terms
representational units
universals
particulars
about
objects
First Order Reality

40. Terminology Realist Ontology
Representation and Reference
concepts
terms
representational units
about
objects
universals
particulars
First Order Reality

41. Terminology Realist Ontology
Representation and Reference
representational units
concepts
terms
cognitive
units
communicative
units
about
objects
universals
particulars
First Order Reality

42. Three levels of reality in Realist Ontology
Terminology Realist Ontology
Representation and Reference
Representational units in various
forms about (1), (2) or (3)
representational units
cognitive
units
communicative
units
(2) Cognitive entities which are our
beliefs about (1)
(1) Entities with objective existence
which are not about anything
universals
particulars
First Order Reality

43. The three levels applied to diabetes management
Generic
Specific
3. Representation
‘person’
‘drug’
‘insulin’
‘W. Ceusters’
‘my sugar’
2. Beliefs (knowledge)
DIAGNOSIS
INDICATION
my doctor’s
work plan
diagnosis 1.
First-order reality me
my blood glucose
level
my NIDDM
my doctor
my doctor’s computer
MOLECULE
PERSON
DISEASE
PATHOLOGICAL
STRUCTURE
PORTION OF
INSULIN
DRUG

44. The three levels applied to cancer cell identification
Generic
Specific 3
‘cancer cell’
‘cell 1’
#6: #5’s belief that
#1 is a cancer cell
#6: #4’s belief that
#1 is not a cancer cell 2
diagnosis
#3: image of
#1 in #2
#1: that debris
#2: framelet 40
#4: that pathologist
#5: this algorithm
debris
image
cancer cell
framelet 1
Weaver DL, Krag DN, Manna EA, Ashikaga T, Harlow SP, Bauer KD.
Comparison of pathologist-detected and automated computer-assisted image analysis detected sentinel lymph node micrometastases in breast cancer. Mod Pathol Nov;16(11): Links

45. Terminology is too reductionist
What concepts do we need?
How do we name concepts properly?

46. The power of realism in ontology design
Reality as benchmark !
1. Is the scientific ‘state of the art’
consistent with biomedical reality ?

47. The power of realism in ontology design
Reality as benchmark !
2. Is my doctor’s knowledge up to date?

48. The power of realism in ontology design
Reality as benchmark !
3. Does my doctor have an accurate
assessment of my health status?

49. The power of realism in ontology design
Reality as benchmark !
4. Is our terminology rich enough
to communicate about all three levels?

50. The power of realism in ontology design
Reality as benchmark !
5. How can we use case studies better
to advance the state of the art?

51. Realist ontology: a modern version of Alberti’s grid !

52. Referent Tracking

53. Requirements for a digital copy of the world
R1: A faithful representation of reality
R2 … of everything that is digitally registered,
what is generic  scientific theories  realism-based ontologies
what is specific  what individual entities exist and how they relate
R3: … throughout reality’s entire history,
R4 … which is computable in order to …
… allow queries over the world’s past and present,
… make predictions,
… fill in gaps,
… identify mistakes,
...

54. The reality: a digital copy of part of the world
Applying the grid should not give a distorted representation of reality, but only an incomplete representation !!!

55. Key issue: keeping track of what the bits denote

56. Fundamental goal of Referent Tracking
explicit reference to the concrete individual entities relevant to the accurate description of each patient’s condition, therapies, outcomes, ...
Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform Jun;39(3):

57. Method: numbers instead of words
Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity
235 78
5678
321
322
666
427
Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform Jun;39(3):

58. IUI: Instance Unique Identifiers
denotes
Relationship managed in the RTS
5241
89023
109427

59. Three basic sorts of relationships
humans are mammals
universal
universal
Werner Ceusters
instance-of at t
human
particular
particular
Werner Ceusters’ nose part-of Werner Ceusters

60. The principle of Referent Tracking
‘John Doe’s
liver
tumor
was treated
with
RPCI’s
irradiation device’
‘John Doe’s ‘John Smith’s
liver liver
tumor tumor
was treated was treated
with with
RPCI’s RPCI’s
irradiation device’ irradiation device’ #1 #3 #2 #4 #5 #6
instance-of at t1
instance-of
inst-of
at t2
treating
person
liver
tumor
clinic
device
#10
#30
#20
#40 #5 #6

61. EHR – Ontology “collaboration”

62. Reasoning over instances and universals
instance-of at t
#105
caused by

63. Essentials of Referent Tracking Theory
Generation of universally unique identifiers;
deciding what particulars should receive a IUI;
finding out whether or not a particular has already been assigned a IUI (each particular should receive maximally one IUI);
using IUIs in the EHR, i.e. issues concerning the syntax and semantics of statements containing IUIs;
determining the truth values of statements in which IUIs are used;
correcting errors in the assignment of IUIs.

64. Requirements for a digital copy of the world
R1: A faithful representation of reality
R2 … of everything that is digitally registered,
what is generic  scientific theories
what is specific  what individual entities exist and how they relate
R3: … throughout reality’s entire history,
R4 … which is computable in order to …
… allow queries over the world’s past and present,
… make predictions,
… fill in gaps,
… identify mistakes,
...

65. Eternal memory

66. Accept that everything may change:
changes in the underlying reality:
Particulars come, change and go

67. Identity & instantiation
child
adult
person t
Living
creature
animal
caterpillar
butterfly

68. Accept that everything may change:
changes in the underlying reality:
Particulars come, change and go
changes in our (scientific) understanding:
The plant Vulcan does not exist

69. Reality and representation: both in evolution
IUI-#3
O-#0
O-#2
Repr.
O-#1
= “denotes”
= what constitutes the meaning of representational units
…. Therefore: O-#0 is meaningless

70. Accept that everything may change:
changes in the underlying reality:
Particulars come, change and go
changes in our (scientific) understanding:
The plant Vulcan does not exist
reassessments of what is considered to be relevant for inclusion (notion of purpose).
encoding mistakes introduced during data entry or ontology development.

71. In John Smith’s Electronic Health Record: What are the possibilities ?
Changes over time
In John Smith’s Electronic Health Record:
At t1: “male” at t2: “female”
What are the possibilities ?
Change in reality:
transgender surgery
change in legal self-identification
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
(Change in word meaning)

72. Requirements for a digital copy of the world
R1: A faithful representation of reality
R2 … of everything that is digitally registered,
what is generic  scientific theories
what is specific  what individual entities exist and how they relate
R3: … throughout reality’s entire history,
R4 … which is computable in order to …
… allow queries over the world’s past and present,
… make predictions,
… fill in gaps,
… identify mistakes,
...

73. Referent Tracking System

74. Referent Tracking System Components
Referent Tracking Software
Manipulation of statements about facts and beliefs
Referent Tracking Datastore:
IUI repository
A collection of globally unique singular identifiers denoting particulars
Referent Tracking Database
A collection of facts and beliefs about the particulars denoted in the IUI repository
Manzoor S, Ceusters W, Rudnicki R. Implementation of a Referent Tracking System. International Journal of Healthcare Information Systems and Informatics 2007;2(4):41-58.

75. Place in the Health IT arena

76. Assertion of assignments
IUI assignment is an act of which the execution has to be asserted in the IUI-repository:
Di = <IUId, Ai, td>
IUId IUI of the registering agent
Ai the assertion of the assignment < IUIp, IUIa, tap>
IUIa IUI of the author of the assertion
IUIp IUI of the particular
tap time of the assignment
td time of registering Ai in the IUI-repository
Neither td or tap give any information about when # IUIp started to exist ! That might be asserted in statements providing information about # IUIp .

77. Elementary RTS tuple types
Relationships between particulars taken from a realism-based relation ontology
Instantiation of a universal
Annotation using terms from a non-realist terminology
‘Negative findings’ such as absences, missing parts, preventions, …
Names for a particular

78. RTS architecture

79. Data store

80. RT templates RDFS schema diagram

81. RTS example graph

82. RT applications

83. eyeGENE (June …)

84. Ontology for Risks Against Patient Safety

85. REMINE: RT-based adverse event analysis

86. Ontology-based IT support for large scale field studies in Psychiatry
assess the functional and technical requirements to be fulfilled by a data management system able to do justice to both the dimensional and categorical approach in psychiatric diagnosis;
design an implementation and funding plan for the technical infrastructure to be built in order to support data collection and analyses in large-scale field studies in psychiatry, and;
initiate the collaborations needed to deliver data collection and analyses services to provide the answers to the questions raised in the DSM-V research agenda.

87. Making existing EHR systems RT compatible

88. RT and student, employee tracking (InfoSource)
Primary goal:
to integrate in stepwise fashion a functional RT system with a set of data from the UB Infosource repository in order to achieve the highest levels of quality and completeness of the data on which the administrative and business needs of the university depend.
Secondary goals:
1) use this experience to optimize the RTS,
2) refine the methodology of deploying the RTS in an enterprise.
3) demonstrate that a limited application of RT is better than no application at all
4) develop an Ontology for Higher Education

89. Semantic integration of the CCW data
Purpose:
Better comparability of Chronic Condition Warehouse data
Statistical validation of the ontology
Explanation of observed correlations between assessment data elements
Finding patient subpopulations exhibiting correlations which are near-significant without the ontology, but significant with the ontology
Two level integration:
Type level : poor man’s linkage
Particular level: rich man’s linkage

90. Codes for types AND identifiers for instances
5572
04/07/1990
closed fracture of shaft of femur
Fracture, closed, spiral
12/07/1990
Accident in public building (supermarket)
79001
Essential hypertension
0939
24/12/1991
benign polyp of biliary tract
2309
21/03/1992
47804
03/04/1993
Other lesion on other specified region
17/05/1993
298
22/08/1993
Closed fracture of radial head
01/04/1997
PtID
Date
ObsCode
Narrative
20/12/1998
malignant polyp of biliary tract
IUI-001
IUI-003
IUI-004
IUI-005
IUI-007
IUI-002
IUI-012
IUI-006
7 distinct disorders

91. Summary (1) Referent Tracking: Particulars include:
Uses IUIs to denote particulars,
Uses realism-based ontologies (3D and 4D) to describe these particulars in relation to each other and to universals.
Particulars include:
Me, this laptop, this room
My beliefs and wishes about the former
Statements registered in the RT Datastore

92. An RT system comes with services to:
Summary (2)
An RT system comes with services to:
Assign IUIs
Annotate reality
Track changes in reality, in our beliefs, and in the status of the RT Data store itself
Reason over instances with or without additional use of computable ontologies.