Download - CLEF: Clinical E-Science Framework
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The CLEF Chronicle: Transforming Patient Records into an E-Science Resource
Jeremy Rogers, Colin Puleston, Alan RectorJames Cunningham, Bill Wheeldin, Jay Kola
Bio-Health Informatics GroupDepartment of Computer Science
University of Manchester
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CLEF: Clinical E-Science Framework
• Improving the storage and processing of Electronic Health Records to enhance general clinical care
• Supporting clinical research via the creation of a clinical research repository, known as the CLEF Chronicle
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WHAT PERCENTAGE OF PATIENTS WHO…
Had cancer with stage of stage-2…located somewhere in the leg…with primary tumour…that doubled in size within a 3 month period
FIRST:
Underwent surgical-intervention to remove all tumours
THEN:
Survived for at least ten years…whilst remaining in remission for the full extent of this period
THEN:
ALSO…
Chronicle Query
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WHAT PERCENTAGE OF PATIENTS WHO…
Had cancer with stage of stage-2 …located somewhere in the leg…with primary tumour…that doubled in size within a 3 month period
FIRST:
Underwent surgical-intervention to remove all tumours
THEN:
Survived for at least ten years…whilst remaining in remission for the full extent of this period
THEN:
ALSO…
Concepts from ExternalKnowledge Sources (EKS)
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Properties from ExternalKnowledge Sources (EKS)
WHAT PERCENTAGE OF PATIENTS WHO…
Had cancer with stage of stage-2 …located somewhere in the leg…with primary tumour…that doubled in size within a 3 month period
FIRST:
Underwent surgical-intervention to remove all tumours
THEN:
Survived for at least ten years…whilst remaining in remission for the full extent of this period
THEN:
ALSO…
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WHAT PERCENTAGE OF PATIENTS WHO…
Had cancer with stage of stage-2 …located somewhere in the leg…with primary tumour…that doubled in size within a 3 month period
FIRST:
Underwent surgical-intervention to remove all tumours
THEN:
Survived for at least ten years…whilst remaining in remission for the full extent of this period
THEN:
ALSO…
mastectomy is-a surgical-intervention
shin part-of lower-leg part-of leg
Implicit RelationshipsBetween EKS Concepts
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WHAT PERCENTAGE OF PATIENTS WHO…
Had cancer with stage of stage-2 …located somewhere in the leg…with primary tumour…that doubled in size within a 3 month period
FIRST:
Underwent surgical-intervention to remove all tumours
THEN:
Survived for at least ten years …whilst remaining in remission for the full extent of this period
THEN:
ALSO…
Temporal Information
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WHAT PERCENTAGE OF PATIENTS WHO…
Had cancer with stage of stage-2 …located somewhere in the leg…with primary tumour…that doubled in size within a 3 month period
FIRST:
Underwent surgical-intervention to remove all tumours
THEN:
Survived for at least ten years …whilst remaining in remission for the full extent of this period
THEN:
ALSO…
ARBITRARY TEMPORAL SEQUENCES
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Temporal Abstractions
WHAT PERCENTAGE OF PATIENTS WHO…
Had cancer with stage of stage-2 …located somewhere in the leg…with primary tumour…that doubled in size within a 3 month period
FIRST:
Underwent surgical-intervention to remove all tumours
THEN:
Survived for at least ten years …whilst remaining in remission for the full extent of this period
THEN:
ALSO…
…whilst remaining in remission for the full extent of this period
…that doubled in size within a 3 month period
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Chronicle System: Overview
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(1) Chronicle Representation
Chronicle Representation
1
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(2) Chronicle Repository + Query Engine
Chronicle Representation
Chronicle Repository
Query Engine
1
2
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(3) ‘Chroniclisation’ Process
Chronicle Representation
Chronicle Repository
Query Engine
Chronicliser
EHR Repository(UCL)
Text Processor (Sheffield)
13
2
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(4) Chronicle Simulator
Chronicle Representation
Chronicle Repository
Query Engine
Chronicle Simulator
Chronicliser
EHR Repository(UCL)
Text Processor (Sheffield)
13
24
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(5) Browser + Query GUIs
Chronicle Representation
Chronicle Repository
Simple Browser +Query Formulator
Query Engine
Query Formulator(Open University)
Chronicle Simulator
Chronicliser
EHR Repository(UCL)
Text Processor (Sheffield)
13
24
5
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ChronicleRepresentation
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Temporal Representation
end point
start point
SPAN Event
occurrence point
SNAPEvent
Time
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Temporal Representation
end point
start point
SPAN Event
occurrence point
SNAPEvent
Note: For the Patient Chronicle the atomic time-unit equals one-day…
Time
…hence, for example, Surgical-Operations and Consultations are SNAP Events
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Temporal Representation
end point
start point
SPAN Event
occurrence point
SNAPEvent
Example: X-ray performed on specific day …with associated
set of results
Time
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Temporal Representation
Time
end point
start point
SPAN Event
occurrence point
SNAPEvent
Example: Period of employment as Plumber, spanning specific time-period
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Temporal Representation
end point
start point
Structured SPAN Event
Time
SNAP SNAPSNAPSNAP
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Temporal Representation
end point
start point
Structured SPAN Event
Time
SNAP SNAPSNAPSNAP
Example: History of Tumour over specific time-period …
…with set of ‘snapshots’ representing same Tumour at specific time-points
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Temporal Representation
end point
start point
Structured SPAN Event
Time
SNAP SNAPSNAPSNAP
Example cont.: Each SNAP has associated value for tumour-size attribute…
…whilst SPAN has set of ‘temporal-abstractions’ (e.g. max, min, etc.) summarising the tumour-size attribute
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Clinical Model
Chronicle Representation
Generic Model
Clinical KnowledgeService
Chronicle Model
Java Object Model
ExternalKnowledge
Sources (EKS)Ontologies,
Databases, etc.
EKS
EKSRelated
Inference
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Clinical Model
Chronicle Representation
Generic Model
EKSRelated
Inference
Clinical KnowledgeService
EKS
Chronicle Representation is embedded within a generic Knowledge Driven Architecture
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Clinical Model
Generic Model
Generic Model
Clinical KnowledgeService
EKS
Including… SNAP/SPAN temporal representation Temporal abstraction mechanisms EKS-concept handling
Generic modelling classes…
EKSRelated
Inference
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Clinical Model
Clinical Model
Generic Model
Clinical KnowledgeService
EKS
Extends generic model with clinical-specific classes
Examples… SNAPS: ProblemSnapshot, SnapClinicalProcedure, etc. SPANS: ProblemHistory, ClinicalRegime, etc.EKS
RelatedInference
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Clinical Model
External Knowledge Sources (EKS)
Generic Model
Clinical KnowledgeService
EKS
Detailed (time-neutral) clinical knowledge sources
Currently: Single OWL ontologyPossibly: Multiple ontologies, databases, etc.
EKSRelated
Inference
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Clinical Model
External Knowledge Sources (EKS)
Generic Model
EKSRelated
Inference
Clinical KnowledgeService
EKS
Provide… Hierarchies of concepts Sets of inter-concept relationships Sets of instance-descriptor properties attached to concepts
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Clinical Model
EKS-Related Inference
Generic Model
EKSRelated
Inference
Clinical KnowledgeService
EKS
Drive… Dynamic data creation Query formulation
Currently: Description-Logic based reasonerPossibly: Rule-bases, procedural code, etc.
Arbitrarily complex inference mechanisms…
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Clinical Model
EKS-Related Inference
Generic Model
EKSRelated
Inference
Clinical KnowledgeService
EKS
Note: Full EKS-related inference is neither appropriate, nor required, for (time-critical) execution of queries over thousands of patient chronicles
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Clinical Model
Clinical Knowledge Service
Generic Model
Clinical KnowledgeService
EKS
Provides transparent access to…External knowledge sourcesEKS-related inference
EKSRelated
Inference
Simple interface…Takes: Instance of concept X, including set of descriptor values
Returns: Updated descriptor-set for X (including updated constraints)
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Problem-Types
ProblemHistory
snapshots[]
ProblemSnapshot
location type
Bodily-Locations
ProblemSnapshotProblem
Snapshot
Chronicle Representation:
ExampleRepresentation of the history of a specific clinical problem* as
displayed by a particular patient
* A ‘problem’ is either a pathology (e.g. cancer) or some
manifestation of a pathology (e.g. a specific tumour)
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Chronicle Model
Objects
Problem-Types
ProblemHistory
snapshots[]
ProblemSnapshot
location type
Bodily-Locations
ProblemSnapshotProblem
Snapshot
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Problem-Types
SPAN Event
SNAP Events
ProblemHistory
snapshots[]
ProblemSnapshot
location type
Bodily-Locations
ProblemSnapshotProblem
Snapshot
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External Knowledge
Sources (EKS)
Problem-Types
ProblemHistory
snapshots[]
ProblemSnapshot
location type
Bodily-Locations
ProblemSnapshotProblem
Snapshot
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‘type’ concept selected from
EKS
ProblemHistory
snapshots[]
ProblemSnapshot
location type
Tumour
ProblemSnapshotProblem
Snapshot
Bodily-Locations
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IntegerHistory
ProblemHistory
snapshots[]
ProblemSnapshot
location type
IntegerSnapshot
tumour-size
IntegerSnapshotInteger
Snapshottumour-size
Tumour
ProblemSnapshotProblem
Snapshot
Bodily-Locations
‘descriptor’ variables derived
from ‘type’ concept
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ProblemHistory
snapshots[]
ProblemSnapshot
location type
IntegerSnapshot
IntegerHistorytumour-size
IntegerSnapshotInteger
Snapshottumour-size
Tumour
value:
time-point:
7
4/3/98
ProblemSnapshotProblem
Snapshot
Bodily-Locations
Values allocated to snapshot ‘descriptors’
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ProblemHistory
snapshots[]
ProblemSnapshot
location type
IntegerSnapshot
IntegerHistorytumour-size
IntegerSnapshotInteger
Snapshottumour-size
Tumour
start-value:
end-value:
minimum:
maximum:
range:
increase-rate:
end-point:
Temporal Abstractions
start-point: 4/3/98
7
7/2/02
43
82
7
75
0.051
ProblemSnapshotProblem
Snapshot
Bodily-Locations
History ‘descriptor’ values derived automatically
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Breast
‘location’ concept selected from EKS
ProblemHistory
snapshots[]
ProblemSnapshot
location type
IntegerSnapshot
IntegerHistorytumour-size
IntegerSnapshotInteger
Snapshottumour-size
Tumour
ProblemSnapshotProblem
Snapshot
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her2-receptor
her2-receptor
Breast
ProblemHistory
snapshots[]
ProblemSnapshot
location type
IntegerSnapshot
IntegerHistorytumour-size
IntegerSnapshotInteger
Snapshottumour-size
Tumour
ProblemSnapshotProblem
SnapshotBoolean
SnapshotBooleanSnapshotBoolean
Snapshot
BooleanHistory
Additional ‘descriptor’ variables inferred via
EKS-related reasoning
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her2-receptor
her2-receptor
Breast
ProblemHistory
snapshots[]
ProblemSnapshot
location type
IntegerSnapshot
IntegerHistorytumour-size
IntegerSnapshotInteger
Snapshottumour-size
Tumour
ProblemSnapshotProblem
SnapshotBoolean
SnapshotBooleanSnapshotBoolean
Snapshot
BooleanHistory
start-value:
end-value:
always-true:
always-false:
percent-true:
percent-false:
end-point:
start-point: 4/3/98
false
7/2/02
true
false
false
63.72
36.28
value:
time-point:
false
4/3/98
Values allocated/derived for new ‘descriptors’
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Chronicle Repositoryand
Query Engine
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Chronicle Query Engine: Requirements
• Querying over Large Numbers of patient chronicles
• Basic RDF/RDFS-Style Reasoning, involving:– Hierarchical relationships (is-a)– Property relationships (part-of, has-location, etc.)– Transitivity
• Temporal Reasoning, including:– Reasoning about temporal sequences– On-the-fly temporal abstraction
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Chronicle Repository
• An RDF/RDFS-based repository (currently using Sesame RDF-store)
• RDF/RDFS representation to facilitate:– Querying over Large Numbers of patient
chronicles– Basic RDF/RDFS Reasoning (must incorporate
transitivity)• Additional Temporal Reasoning mechanisms
will be required (including on-the-fly temporal abstraction)
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ChroniclisationProcess
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Electronic Health Records (EHR)
• Document based:– One document per clinical procedure
• Minimally structured:– No inter-concept references– No inter-document references
• Mainly free-form text:– For human consumption– Incomplete information– Many implicit assumptions
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Chroniclisation
• Complex heuristic process:– Input: Largely unstructured EHR data– Output: Highly structured chronicle data
• Process will involve:– Text processing– Co-reference resolution– Temporal reference resolution – Inference of implicit information
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CLEF Chronicle: Summary
• Chronicle Representation:– Temporal Representation– External Knowledge Sources (OWL, etc.)– Complex EKS-related reasoning (DL, etc.)
• Chronicle Repository + Query Engine:– Querying large numbers of patient records– Simple EKS-related reasoning (RDF/RDFS)– Temporal Reasoning
• Chroniclisation Process:– Input: Largely unstructured EHR data– Output: Highly structured Chronicle data