Download slides - Referent Tracking Unit

R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Grand Rounds, Psychiatry
Advancing Translational Research
in Psychiatry through
Realism-based Ontology
UVM College of Medicine - Department of Psychiatry
Fletcher Allen Health Care
Burlington, VT, USA, August 15, 2008
Werner CEUSTERS, MD
Center of Excellence in Bioinformatics and Life Sciences
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
1977
1959
-
2008
2006
Short personal
history
1989
2004
1992
2002
1995
1993
1998
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Past work and research in Psychiatry
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1990-1991
1990-1993
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1990
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1990
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1991-1992
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1991-1999
1994-1998
•
Ceusters W, Van de Wiele L, Van Moffaert M. Therapeutical value of an automated
observation scale for psychiatric inpatients, in MIC-Proceedings 1990, Noordwijkerhout, The
Netherlands, Nov 9-10, 1990;:212-23.
Ceusters W, De Cuypere G, Jannes C, Hoes M, Pluymakers J. Rational and efficient use of
computers in psychiatry: the AMDP as a European standard for psychiatric record systems ?
in: Proc MIC 1991, Willems JL (ed.), 1991;:117-126.
•
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Head of Department of Neuropsychiatry, Military Hospital Soest, Germany.
Consultant in Medical Informatics, Department of Psychiatry, University
Hospital of Ghent.
Egmond Prize for Medical Informatics (1990) (for automated observation scale for
psychiatric in-patients).
Dutch and Belgian Medical Informatics Conference Prize of the Belgian
Society for Medical Informatics (for best paper and presentation).
Member, Belgian Ministry of Health Working Group on the Registration of
Psychiatric Patients.
Neuropsychiatrist, Queen Astrid Military Hospital of Brussels.
Member, Mental Health Standards Working Group of WHO, Division of
Mental Health.
Ceusters W, Smith B. Referent Tracking for Treatment Optimisation in Schizophrenic
Patients. Journal of Web Semantics 4(3) 2006:229-36;
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Ongoing Research in Psychiatry
• ‘UB Task Force for ontology-based IT support for large
scale field studies in Psychiatry’
– Sponsor: John R. Oishei Foundation ($148,328)
– Specific aims:
• to 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;
• to 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;
• to 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.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
What has this research to do with
translational medicine ?
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Presentation overview
•
•
•
•
Some key aspects of Translational Research
Philosophy & Psychiatry
Ontology & Informatics
Connecting the dots: a holistic approach to
Evidence Based Medicine (in Vermont)
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
I. Translational Research
What is it ?
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Translational Research
• Research in which ideas, insights, and discoveries
generated through basic scientific inquiry are
applied to the treatment or prevention of human
disease.
• Two categories:
– T1: from ‘bench to bedside’
– T2: from bedside to community: enhance adoption of
effective programs and practices
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Further distinctions
translation
to humans
translation
to patients
translation
to practice
Westfall, J. M. et al. JAMA 2007;297:403-406.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Translational Research & NIMH
• Division of Developmental Translational Research
– promotes an integrated program of research across basic
behavioral/psychological processes, environmental processes, brain
development, pediatric psychopathology and therapeutic interventions.
• Division of Adult Translational Research and Treatment
Development
– understanding the pathophysiology of mental illness and
hastening the translation of behavioral science and neuroscience
advances into innovations in clinical care.
• Geriatric Translational Neuroscience Program
• Geriatric Translational Behavioral Science Program
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Translational psychiatry
Cellular events
behavior
cognitive functionings
• Forward translational psychiatry:
– attempts to explain how neuronal activity, beginning at the
molecular level, 'translates' to elicitation of behavior
• Reverse translational psychiatry:
– attempts to determine the molecular underpinnings that
contribute to the expression of abnormal behavior.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A key challenge: understanding how disorders at molecular
level lead to disorders at mesoscopic level
www.thefencingpost.com/mary/
www.williams.ngo.hu/
medgen.genetics.utah.ed
u/.../pages/williams.htm
http://www.williams-syndrome.org/
Williams Syndrome: a rare genetic disorder characterized by mild to
moderate mental retardation or learning difficulties, a distinctive facial
the
? overfriendliness
appearance,Any
and aideas
unique about
personality
thatabove
combines
and high levels of empathy with anxiety.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Another key challenge: multi-disciplinarity
e.g.: Translational Research and the cause of Alzheimer Disease
•
•
•
•
•
•
Disciplines
mouse genetics
cell biology
animal
neuropsychology
protein
biochemistry
neuropathology
…
•
•
•
•
Hypotheses
ADDL
Amyloid cascade
Alternative
amyloid cascade
…
Difficult process
Conflicting outcomes
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Barriers for Translational Research
Sung NS et al. Central challenges facing the national clinical research enterprise. JAMA. 2003;289:1278–1287.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Common approach: semantic annotation of scientific data
<ms/ms_peak_list>
<parameter
instrument=“micromass_QTOF_2_quadropole_time_of_fli
ght_mass_spectrometer”
mode = “ms/ms”/>
<parent_ion_mass>830.9570</parent_ion_mass>
<total_abundance>194.9604</total_abundance>
<z>2</z>
<mass_spec_peak m/z = 580.2985 abundance = 0.3592/>
<mass_spec_peak m/z = 688.3214 abundance = 0.2526/>
<mass_spec_peak m/z = 779.4759 abundance = 38.4939/>
<mass_spec_peak m/z = 784.3607 abundance = 21.7736/>
<mass_spec_peak m/z = 1543.7476 abundance = 1.3822/>
<mass_spec_peak m/z = 1544.7595 abundance = 2.9977/>
<mass_spec_peak m/z = 1562.8113 abundance =
37.4790/>
<mass_spec_peak
m/zms/ms
= 1660.7776
abundance
=
Annotated
peaklist
data
476.5043/>
<ms/ms_peak_list>
Amit Sheth. Semantic Web Technology in Support of Bioinformatics for Glycan Expression.
W3C workshop on Semantic Web for Life Sciences, October 28, 2004, Cambridge MA
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Lost in translation
Various reporting formalisms and data formats
Various levels of granularity
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Not the least in … psychiatry
BTP:
BTR:
BPR:
Barriers for translational psychiatry
=
barriers for translational research
op?
barriers for psychiatric research
BTP = BTR+BPR or BTR*BPR or BTR(BPR) ?
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
II. Philosophy & Psychiatry
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A little test:
who said …
• “Too little attention has been paid in psychiatric
education and training to the philosophical
underpinnings of our field, and we believe that
many problems with the way in which psychiatry is
both perceived from the outside and practiced
from the inside are attributable to a lack of clarity
- or simply an absence of thought - on this topic.”
Waterman, GS. & Schwartz, RJ.
The Mind-Body Problem. Letter to the Editor.
Am J Psychiatry 159:878-879, May 2002
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Ontology and Psychiatry
• Ontology:
– (roughly) the branch of philosophy that deals with what exists
and with how the entities that exist relate to each other.
– representing reality in IT systems
• Psychiatry:
– (roughly) the branch of medicine that deals with the diagnosis,
treatment, and prevention of ‘mental and emotional disorders’.
• Ontology applied to psychiatry:
– Studying the nature of ‘mental disorders’ and their place in
pathological anatomy and pathophysiology;
– Finding better ways to build IT systems to support research in
and practice of psychiatry.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
‘Ontology’ as the study of what exists
• Key questions:
– What exists ?
– How do things that exist relate to each other ?
• Some hypotheses:
–
–
–
–
An external reality, time, space
Ideas, concepts
Particulars, universals, objects, processes
God
• Ontologists from distinct ‘schools’ differ in opinion about
the existence of some of the above:
– Realism, nominalism, conceptualism, monism, …
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A parallel: some wonder whether ‘mental disorders’ exist
The
Antipsychiatry
Coalition
• ‘there are no biological abnormalities
responsible for so-called mental illness,
mental disease, or mental disorder,
therefore mental illness has no biological
existence.
• Perhaps more importantly, however,
mental illness also has no non-biological
existence,
• except in the sense that the term is used
to indicate disapproval of some aspect of
a person's mentality.’
Lawrence Stevens, J.D, 1999
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
The “Myth of Mental Illness”
• “I maintain
– that the mind is not the brain,
– that mental functions are not reducible to brain functions, and
– that mental diseases are not brain diseases,
– indeed, that mental diseases are not diseases at all.
• When I assert the latter, I do not imply that distressing personal
experiences and deviant behaviors do not exist. Anxiety, depression,
and conflict do exist--in fact, are intrinsic to the human condition-but they are not diseases in the pathological sense.”
Thomas S. Szasz (MD), Mental Disorders Are Not Diseases.
USA Today (Magazine) January 2000
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Another parallel: the ‘categorical – dimensional’ debate
on the classification of mental disorders
• Rough distinction:
– “Categorical”: ‘mental disorders’ can be classified as single,
discrete and mutually exclusive types, of which a particular
patient does or does not exhibit an instance.
DSM
– “Dimensional”: any particular ‘mental disorder’ in a patient is
an instance of just one single type and differences between cases
are a matter of ‘scale’.
• ‘Rough’, because
– the literature is huge and vague
– descriptions are (philosophically) very incoherent
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
DSM under fire
• severely ill inpatients often meet criteria for more than
one DSM-IV personality disorder;
• many outpatients do not meet the criteria for any of the
specific categories identified in DSM-IV;
• patients with the same categorical diagnosis often vary
substantially with respect to signs and symptoms;
• frequent revision of the diagnostic thresholds separating
what is normal from what is disordered;
• a number of the diagnostic categories mentioned in DSMIV lack any developing scientific base for an
understanding of the corresponding disorder types.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Medical Models and the Dimensions of Categorization
• ‘Theories about […] psychiatric disorders […] often contain a range of
assumptions about what counts as real, valid, relevant, and useful. They
also often assume different notions about the nature of causal processes in
psychiatric illness.’
Zachar P, Kendler KS. Psychiatric Disorders: A Conceptual Taxonomy.
Am J Psychiatry 2007; 164:557–565
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
But: some dimensionalists also use flawed arguments
• “Diagnostic categories defined by their syndromes should
be regarded as valid only if they have been shown to be
discrete entities with natural boundaries that separate
them from other disorders.”
Kendell R, Jablensky A. Distinguishing between the validity and
utility of psychiatric diagnoses. Am J Psychiatry 2003; 160:4–12.
• “there is no empirical evidence for natural boundaries
between major syndromes” … “the categorical approach
is fundamentally flawed”
Cloninger CR: A new conceptual paradigm from genetics and psychobiology
for the science of mental health. Aust N Z J Psychiatry 33:174–186, 1999.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Is there empirical evidence for this boundary ?
And if not, do these mountains exist ?
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Key issue: constructs & reality
www.perseus.tufts.edu/.../Hpix/1992.06.1227.jpeg
mcgonnigle.files.wordpress.com/.../lightning.jpg
Just as what used to be seen as Zeus’s thunderbolts can still be
lethal, what is currently referred to as mind can certainly be—and
clearly is—causally efficacious.
Waterman, GS. & Schwartz, RJ.
The Mind-Body Problem. Letter to the Editor.
Am J Psychiatry 159:878-879, May 2002
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Attempts to resolve the problem (1)
• Mental disorders as ‘practical kinds’
– ‘stable patterns that can be identified with varying
levels of reliability and validity’ and which are justified
by their usefulness for specific purposes – such as
giving an appropriate treatment
Zachar, P. 2000b. Psychiatric disorders are not natural kinds.
Philosophy, Psychiatry and Psychology 7:167–94.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Basis: ‘epistemic value commitments’
• ‘values involved in making and advancing
epistemologically-relevant claims, such as
scientific ones’:
Coherence
Instrumental efficacy
Consistency
Originality
Comprehensiveness Relevance
Fecundity
Precision
Simplicity
JZ. Sadler. Epistemic Value Commitments in the Debate over Categorical vs. Dimensional
Personality Diagnosis. Philosophy, Psychiatry, & Psychology 3.3 (1996) 203-222
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Attempts to resolve the problem (2)
Non-arbitrary basis for drawing a categorical distinction
No
Yes
Non-kind
‘severity’
‘neuroticism’
This basis is an objective discontinuity
No
Yes
Practical kind
‘essential hypertension’
‘depression’
Haslam N. Kinds of Kinds: A
Conceptual Taxonomy of
Psychiatric Categories.
Philosophy, Psychiatry, &
Psychology, 9 (2002), 203-218
The discontinuity is sharp and binary
No
Yes
Fuzzy kind
‘borderline personality’
The discontinuity is constituted by an ‘essence’
No
Yes
Discrete kind
‘melancholia’
Natural kind
‘Williams syndrome’
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
DSM-IV-TR currently plays it both ways
• “In DSM-IV, there is no assumption that each
category of mental disorder is a completely
discrete entity with absolute boundaries dividing it
from other mental disorders or from no mental
disorder”
• “DSM-IV is a categorical classification that
divides mental disorders into types based on
criterion sets with defining features”
Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text
Revision [DSM-IV-TR]; AmericanPsychiatric Association [APA], 2000, p. xxxi).
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
… but asks for research in preparation of DSM-V
• Some desiderata:
1. generate acceptable definitions for mental disorder; disease and
illness;
2. provide a framework for validating the correctness of
assignments of instances to disorder categories;
3. provide assessment of the arguments to the effect that a
dimensional view is needed in addition to the categorical view;
4. reduce the discrepancies between DSM-V and ICD-11;
5. ensure that DSM-V can be used cross-culturally;
6. ensure that DSM-V can be used in non-psychiatric settings.
Kupfer DJ, First MB, Regier DA (eds.) A Research Agenda for DSM-V.
American Psychiatric Association 2002.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
… but asks for research in preparation of DSM-V
• to establish, among many other things,
– under which circumstances one or the other of the two views
should be adopted,
– the categories which will then need to be recognized, and
– the thresholds for associated criteria.
• The proposed research is to be based on large scale crosscultural clinical, genetic, pathophysiologic, etiologic and
outcome assessments,
and thus requires the collection of vast
amounts of data of diverse sorts.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
III. Ontology and Informatics
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Three major views on reality
Realism
• Basic questions:
– What does a
general term
such as
‘psychosis’ refer
to?
– Do generic
things exist?
Conceptualism Nominalism
Universal
Concept
Collection
of
particulars
yes: in
particulars
perhaps: in
minds
no
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Types of realism
• Naive realism:
– things really are as they seem
• Scientific realism:
– things really are as science determines (or ultimately
will determine) them to be
– science discovers objective truths
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Scientific Research (realism-based)
Reality
universals
particulars
William M.K. Trochim. Idea of Construct Validity.
Research Method Knowledge Base 2006.
http://www.socialresearchmethods.net/kb/considea.php
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Realism-based ontology
• Basic assumptions:
1. reality exists objectively in itself, i.e. independent of
the perceptions or beliefs of cognitive beings;
2. reality, including its structure, is accessible to us,
and can be discovered through (scientific) research;
3. the quality of an ontology is at least determined by
the accuracy with which its structure mimics the
pre-existing structure of reality.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Three levels of reality in Realist Ontology
Representation and Reference
representational units
(3) Representational units in various
forms about (1), (2) or (3)
cognitive
units
communicative
units
universals
particulars
(2) Cognitive entities which are our
beliefs about (1)
(1) Entities with objective existence
which are not about anything
First Order Reality
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Realist ontology: a modern version of Alberti’s grid !
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Application in preparation of DSM-V (1)
• For each ‘mental disorder’
– Express the criteria in terms of the core ontological
entities and their possible co-occurrence in concrete
cases
• For each particular case (‘disorder in patient’)
– Describe the case using the core ontological entities
and their actual co-occurrence, i.e.
• Assign IUIs
• Express in RT-formalism
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Application in preparation of DSM-V (2)
• Create an adequate IT infrastructure:
– For case registration: RTU-based electronic patient
record
– For data collection: RTU back-end
• Use the DSM-criteria as hypotheses that need to
be validated on the basis of the data collected, and
adjust when needed.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
IV. Connecting the dots in Vermont
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Vermont Health Information Technology Plan
• Core objectives for 2007-2012:
– increase the amount of health information that exists in
electronic form, i.e. in electronic health record systems
– achieve a secure electronic health information
exchange to achieve the plan’s vision.
– empower consumers to take an active role in electronic
health information initiatives in Vermont.
– enable public health agencies to use HIT to monitor
and ensure the public’s health more transparently and
quickly
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Some key choices identified in VHITP
• Appropriate national standards for technical, semantic,
and process interoperability;
– But cave: too many “standards” for semantic interoperability
exhibit one or more problems:
•
•
•
•
very superficial semantics
bad design
no solid foundation (lack a serious benchmark)
not compatible with other standards
• A hybrid technology architecture with both centralized
and distributed data service components;
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Adoption of
the (Chronic)
Care Model
Wagner EH, Austin BT, Davis C,
Hindmarsh M, Schaefer J, Bonomi A.
Improving chronic illness care:
translating evidence into action.
Health Aff (Millwood). 2001;20:64
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
CCM: Clinical Information Systems
• Provide reminders for providers and patients
• Identify relevant patient subpopulations for
proactive care
• Facilitate individual patient care planning
• Share information with providers and patients
• Monitor performance of team and system
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
ACIC 3.5: Clinical Information Systems Scores
Components
D
Level C
Level B
Level A
Registry
…includes name, diagnosis, contact
information and date of last contact either
on paper or in a computer database.
…allows queries to sort subpopulations by clinical priorities.
…is tied to guidelines which
provide prompts and reminders
about needed services.
Reminders to
Providers
… include general notification of the
existence of a chronic illness, but does
not describe needed services at time of
encounter.
…includes indications of needed
service for populations of patients
through periodic reporting.
…includes specific information
for the team about guideline
adherence at the time of
individual patient encounters.
Feedback
…is provided at infrequent intervals and is
delivered impersonally.
…occurs at frequent enough
intervals to monitor performance
and is specific to the team’s
population.
…is timely, specific to the
team, routine and personally
delivered by a respected
opinion leader to improve team
performance.
Relevant
Subgroups of
Patients Needing
Services
…can only be obtained with special efforts
or additional programming.
…can be obtained upon request
but is not routinely available.
…is provided routinely to
providers to help them deliver
planned care.
Patient
Treatment Plans
…are achieved through a standardized
approach.
…are established collaboratively
and include self management as
well as clinical goals.
…are established collaborative
an include self management as
well as clinical management.
Follow-up occurs and guides
care at every point of service.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
VITL's EHR Pilot Project
Vermont
Information
Technology
Leaders Inc.’s
Pre-Screened EHR
Product List
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Getting it all: Referent Tracking
Portion of Reality
Entity
Configuration
represents
Universal
Particular
contains
is about
Non-referring
particular
Information bearer
corresponds-to
Representation
RT-tuple
Defined class
Representational unit
Denotator
denotes
CUI
IUI
UUI
denotes
denotes
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
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.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Referent Tracking System Environment
User
User
External
Information
System
Referent Tracking System
IUI
Component
RTS
Proxy
Peer
Referent Tracking System User Interface(s)
Referent Tracking Server (Peers)
Reasoning Server
Referent Tracking Data Access
Server
RTS
Server
Proxy
Peer
Internal Ontology
Referent Tracking Data
Store
Terminology Server
Vocabulary
or
Thesaurus
or
Nomenclature
or
Concept System
or
Realism-based
Ontology
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Summary
• Translational Research is a must, but has many barriers,
especially in psychiatry.
– Key issue: the problem of “emergence”
• ‘mind’ emerging from brain activities
• Philosophy, especially realism-based ontology, provides
methods and techniques to deal with the issues.
• Referent Tracking, as an implementation of realism-based
ontology, is able to keep track of how scientific insights
and individual care evolve over time.
• Key players in Vermont understand what is at stake and
are moving in the right direction.
• Vermont is thus an ideal environment for crossfertilization.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Additional slides
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Reality-based mapping of care assessment instruments
BFO
U16
Class-relations
U1
U17
U2
U9
U8
U3
MDS
Ontology
U14
U7
U10
U4
U12
U6
U5
U13
U11
MDS terms
MDS1
MDS2
MDS3
MDS4
MDS5
MDS6
…
State
R T U New York
Adding
the other assessments instruments
Center of Excellence in
Bioinformatics & Life Sciences
BFO
U16
U1
U17
U2
U9
U8
U3
U…
U7
U10
U4
U12
U6
U5
MDS1
MDS2
OPO
Ontology
(MDS + CARE +…)
U14
U13
U11
…
MDS3
MDS4
MDS5
MDS terms
MDS6
…
New York State
R
T
U
Adding the other assessments instruments
CARE terms
Center of Excellence in
Bioinformatics & Life Sciences
U15
U1
U17
U2
U…
U9
U8
U3
BFO
U16
U7
U10
U4
U12
U6
U5
…
MDS1
MDS2
OPO
Ontology
(MDS + CARE +…)
U14
U13
U11
…
MDS3
MDS4
MDS5
MDS terms
MDS6
…
R T U New York StateU
‘Poor man’s’ data linkage
16
Center of Excellence
in
Bioinformatics & Life Sciences
U1
U17
U2
U9
U8
U3
U…
U7
U10
U4
U12
U6
U5
MDS1
MDS
Ontology
U14
U13
U11
…
MDS2
MDS3
pt4
MDS4
MDS5
pt3
MDS terms
MDS6
…
Patient
data
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Data linkage using multiple instruments
CARE terms
U15
1
A
R
E
s
C
X
C
A
R
E
X
U1
U17
C
U3
X
U7
U10
U12
4
C
X
U6
X
X
MDS1
X
…
MDS3
MDS2
MDS4
X
X
X
X
X
X
U13
U11
U5
…
OPO
Ontology
(MDS + CARE +…)
U14
U4
A
R
E
U9
U8
3
E
U2
A
R
X
U…
2
nt
tie
a
P
BFO
U16
X
X
MDS5
X
MDS terms
MDS6
…
Patient 1
Patient 2
Patient 3
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Problems with this level
• Exclusive focus on universals, ignoring that in
data collection (almost) everything is about
particulars.
• Therefore Referent Tracking must be brought in
the picture.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Referent Tracking solves this problem:
• It is true that:
– (1) ‘All Americans have one mother’
– (2) ‘All Americans have one president’
• But:
– (1) ‘all Americans have a distinct mother’
– (2) ‘all Americans have a (numerically) identical
president’
R T U New York StateU
From ‘poor man’s’ to
16
Center of Excellence
in
‘rich man’s’ data linkage
Bioinformatics & Life Sciences
U1
U17
U2
U9
U8
U3
U7
U10
U4
U12
U6
formula
MDS1
MDS
Ontology
U14
U5
U13
U11
MDS terms
MDS2
MDS3
pt4
MDS4
MDS5
pt3
MDS6
…
Patient
data
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Rich man’s data linkage: focus on particulars
U6
U6
MDS3
pt4
U11
U11
MDS4
Instance-of
IUI-1
IUI-2
IUI-3
IUI-4
Particular
relations
pt3
pt4
pt3
IUI-5
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Many more combinations possible
U6
IUI-1
IUI-2
pt4
IUI-3
U6
U11
IUI-4
IUI-5
pt3
• The terms used in MDS4
denote distinct particulars
related to both patients
IUI-1
IUI-3
pt4
IUI-2
U11
IUI-5
pt3
• One of the terms used in
MDS4 denotes the same
particular for both patients
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Many more combinations possible
• If the same MDS (containing several referring terms)
applies to different patients at t1, either
– All terms denote always distinct particulars
• ‘patient is able to recall what he did yesterday’
– Some terms denote the same particular
• ‘patient is able to remember who he met yesterday’
• If the same MDS applies to the same patient at distinct
times:
– Some terms may/may not denote the same particular
• ‘patient recognizes his room mate’
• If the same term occurs in distinct MDS
– May/may not denote the same particular (at any time)