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Ontology and the future of Evidence-Based Medicine Dagstuhl May 23th, 2006

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Title: Ontology and the future of Evidence-Based Medicine Dagstuhl May 23th, 2006


1
Ontology and the future ofEvidence-Based
Medicine Dagstuhl May 23th, 2006
  • Werner Ceusters, MD
  • Ontology Research Group
  • Center of Excellence in Bioinformatics Life
    Sciences
  • SUNY at Buffalo, NY

2
Evidence Based Medicine
  • the integration of best research evidence with
    clinical expertise and patient values.
  • best research evidence clinically relevant
    patient centered research into the accuracy and
    precision of diagnostic tests, the power of
    prognostic markers, and the efficacy and safety
    of care regimens.
  • clinical expertise the ability to use
    clinical skills and past experience to rapidly
    identify each patient's unique health state.
  • patient values the unique preferences,
    concerns and expectations each patient brings to
    a clinical encounter and which must be integrated
    into clinical decisions if they are to serve him.

3
Application of Evidence Based Medicine
  • Now
  • Decisions based on (motivated/justified by) the
    outcomes of (reproducable) results of
    well-designed studies
  • Guidelines and protocols
  • Evidence is hard to get, takes time to
    accumulate.
  • Future
  • Each discovered fact or expressed belief should
    instantly become available as contributing to
    evidence, wherever its description is generated.

4
Future scenarios
  • Data entered about a successful treatment of a
    case in X generates a suggestion for a similar
    case in Y
  • Submission of a new paper to Pubmed on some ADR
    triggers an alert in EHR systems worldwide for
    those patients that might be at risk
  • ? From reactive care to proactive care

5
Some problem areas
  • Pharmaceutical Industry
  • Optimise drug discovery
  • Make messy databases more useful for everybody
  • Consumer health
  • Opposing forces
  • Quality of information
  • Make them consume
  • Malpractice suits
  • Public sector health
  • Cost containment
  • Cost effectiviness of treatment, prevention
  • Bio-informatics world
  • How to find out that a discovery is a new
    discovery ?

6
An action plan for a European eHealth Area.
  • By the start of 2005
  • MS and EC should agree on an overall approach to
    benchmarking in order to assess the quantitative,
    including economic, and qualitative impacts of
    e-Health.
  • By end 2006
  • in order to achieve a seamless exchange of health
    information across Europe through common
    structures and ontologies, MS, in collaboration
    with the EC, should identify and outline
    interoperability standards for health data
    messages and electronic health records, taking
    into account best practices and relevant
    standardisation efforts.
  • By end 2008
  • the majority of all European health organisations
    and health regions (communities, counties,
    districts) should be able to provide online
    services such as teleconsultation (second medical
    opinion), e-prescription, e-referral,
    telemonitoring and telecare.

Failed
7
One key issue Semantic Interoperability
  • Working definition
  • Two information systems are semantically
    interoperable if and only if each can carry out
    the tasks for which it was designed using data
    and information taken from the other as
    seemlessly as using its own data and information.

system Any organized assembly of resources and
procedures united and regulated by interaction or
interdependence to accomplish a set of specific
functions.
information system a system, whether automated
or manual, that comprises people, machines,
and/or methods organized to collect, process,
transmit, and disseminate data that represent
user information.
8
Essential components
Communication Interpretation
  • People physicians, nurses, patients, healthcare
    administrators, ...
  • Machines
  • to make humans interact with the EHR,
  • to transmit data from one EHR to another
  • to enter data (lab analysers, EMR monitors, ...)
  • to interprete data (alerts, quality assessment,
    protocol selection, ...)
  • Data and information (data in context)
  • Procedures

9
Understanding content (1)
John Doe has a pyogenic granuloma of the left
thumb
John Doe has a pyogenic granuloma of the left
thumb
10
Understanding content (2)
ltrecordgt ltpatientgtJohn Doelt/patientgt ltdiagnosisgtpy
ogenic granuloma of the left thumblt/diagnosisgt lt/r
ecordgt
ltrecordgt ltsubjectgt John Doe lt/subjectgt ltdiagnosisgt
pyogenic granuloma of the left thumb
lt/diagnosisgt lt/recordgt
11
Understanding content (3)
lt129465004gt lt116154003gtJohn Doelt/116154003gt lt
8319008 gt 17372009 ltfinding sitegt 76505004
ltlateralitygt7771000lt/lateralitygt lt/finding
sitegt lt/ 8319008 gt lt/129465004gt
12
Ontology based on Unqualified Realism
  • Accepts the existence of
  • a real world outside mind and language
  • a structure in that world prior to mind and
    language (universals / particulars)
  • Rejects nominalism, conceptualism, ontology as a
    matter of agreement on conceptualizations
  • Uses reality as a benchmark for testing the
    quality of ontologies as artifacts by building
    appropriate logics with referential semantics
    (rather than model-theoretic)

13
Relevance for EHR Semantic Interoperability
14
Relevance for EHR Semantic Interoperability
The realist approach
R E A L I T Y
L O G O L K A I S N S G
Ontology
EHR
15
Terminology
  • A theory concerned with those aspects of the
    nature and the functions of language which permit
    the efficient representation and transmission of
    items of knowledge (J. Sager)
  • Precise and appropriate terminologies provide
    important facilities for human communication (J.
    Gamper)

16
Ontology
  • An ontology is a representation of some
    pre-existing domain of reality which
  • (1) reflects the properties of the objects 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

17
A division of labour
  • Terminology
  • Communication amongst humans
  • Communication between human and machine
  • Ontology
  • Representation of reality inside a machine
  • Communication amongst machines
  • Interpretation by machines

18
Todays biggest problem a confusion between
terminology and ontology
  • The conditions to be agreed upon when to use a
    certain term to denote an entity, are often
    different than the conditions which make an
    entity what it is.
  • Trees would still be different from rabbits if
    there were no humans to agree on how these things
    should be called.
  • ontos means being. The link with reality
    tends to be forgotten one concentrates on the
    models instead of on the reality.

19
What to do about it ? (1)
  • Research
  • Revision of the appropriatness of concept-based
    terminology for our purposes
  • Relationship between models and that part of
    reality that the models want to represent
  • Adequacy of current tools and languages for
    representation
  • Boundaries between terminology and ontology and
    the place of each in semantic interoperability in
    healthcare

20
What to do about it ? (2)
  • Training and awareness
  • Make people more critical wrt terminology and
    ontology promisses
  • What is needed must be based on needs, not on the
    popularity of a new concept
  • But in a system, its not just your own needs, it
    is each components needs !
  • Towards an ontology of ontologies
  • First description
  • Then quality criteria

21
Ultimate goal
Ontology
continuant
disorder
person
CAG repeat
EHR
Juvenile HD
IUI-1 affects IUI-2 IUI-3 affects
IUI-2 IUI-1 causes IUI-3 ...
Referent Tracking Database
22
3 fundamentally different in levels
  • the reality on the side of the patient
  • the cognitive representations of this reality
    embodied in observations and interpretations on
    the part of clinicians and others
  • the publicly accessible concretizations of such
    cognitive representations in representational
    artifacts of various sorts, of which ontologies
    and terminologies are examples.

23
Example a person (in this room) s phenotypic
gender
  • Reality
  • Male
  • Female
  • Cognitive representation
  • male
  • female
  • In the EHR
  • male
  • female
  • unknown

24
4 fundamental reasons for making changes
  • changes in the underlying reality
  • does the appearance of an entry (in a new version
    of an ontology or in an EHR) relate to the
    appearance of an entity or a relationship among
    entities in reality ?
  • changes in our (scientific) understanding
  • reassessments of what is considered to be
    relevant for inclusion (notion of purpose), or
  • encoding mistakes introduced during data entry or
    ontology development.

25
Key requirement
  • Any change in an ontology or data repository
    should be associated with the reason for that
    change !

26
Example a person (in this room) s gender in the
EHR
  • In John Smiths EHR
  • At t1 male at t2 female
  • What are the possibilities ?
  • Change in reality transgender surgery
  • Change in understanding it was female from the
    very beginning but interpreted wrongly
  • ( No change in relevance )
  • Correction of data entry mistake
  • (was understood as male, but wrongly transcribed)

27
Possible combinations
OE objective existence ORV objective
relevance BE belief in existence BRV
belief in relevance Int. intended encoding
Ref. manner in which the expression refers
G typology which results when the factor of
external reality is ignored. E number of
errors when measured against the benchmark of
reality. P/A presence/absence of term.
28
Possible evolutions

29
Towards an implementation
  • A client-server application in which the server
    is composed of four layers
  • the Web Server Layer (WSL) provides the interface
    to clients via web services
  • the RT Core application programming interface
    (API) encapsulates the data services related to
    storage and retrieval. Its Security Module
    validates the access rights before any data
    service
  • the database layer stores all the RT data, and
  • the reasoner layer (RL) performs inferences upon
    specific requests, based on the information
    available in the database and, if available, the
    ontologies that have been used for the
    descriptions of the portions of reality.

Shahid Manzoor
30
Schematic representation
31
Simple Graph Representation
32
Complete structure
33
UML-diagram for the entities in the RDF-graph
34
Querying the RTDB using ontologies (SPARQL)
Retrieve particulars that are related to the
universal face
1 PREFIX rts lthttp//ecorgt 2 PREFIX fma
lthttp//FMAgt 3 SELECT ?p ?u ?v 4 WHERE ?p
rtsrelation ?u . 5 ?u a rtsPtoU . 6 ?u rtsu
?t . 7 ?t a fmaFace . 8
35
Querying the RTDB using ontologies (SPARQL)
1 WHERE ?p rtsrelation ?rf . 2 ?rf a rtsPtoP
. 3 ?rf rtsp ?f . 4 ?f a fmaHead . 5 ?f
rtsrelation ?rd . 6 ?rd a rtsPtoP . 7 ?rd p
?d . 8 ?d a disDISEASES AND INJURIES .
Retrieve patients with diseases in the head
36
Test interface
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