the clef chronicle: transforming patient records into an e-science resource jeremy rogers, colin...

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

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

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


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:


THEN:


THEN:

ALSO…

Concepts from ExternalKnowledge Sources (EKS)

Properties from ExternalKnowledge Sources (EKS)



FIRST:


THEN:


THEN:

ALSO…



FIRST:


THEN:


THEN:

ALSO…

mastectomy is-a surgical-intervention

shin part-of lower-leg part-of leg

Implicit RelationshipsBetween EKS Concepts



FIRST:


THEN:

Survived for at least ten years …whilst remaining in remission for the full extent of this period

THEN:

ALSO…

Temporal Information



FIRST:


THEN:


THEN:

ALSO…

ARBITRARY TEMPORAL SEQUENCES

Temporal Abstractions



FIRST:


THEN:


THEN:

ALSO…

…whilst remaining in remission for the full extent of this period

…that doubled in size within a 3 month period

Chronicle System: Overview

(1) Chronicle Representation

Chronicle Representation

1

(2) Chronicle Repository + Query Engine


Chronicle Repository

Query Engine

1

2

(3) ‘Chroniclisation’ Process



Query Engine

Chronicliser

EHR Repository(UCL)

Text Processor (Sheffield)

13

2

(4) Chronicle Simulator



Query Engine

Chronicle Simulator

Chronicliser

EHR Repository(UCL)


13

24

(5) Browser + Query GUIs



Simple Browser +Query Formulator

Query Engine

Query Formulator(Open University)

Chronicle Simulator

Chronicliser

EHR Repository(UCL)


13

24

5

ChronicleRepresentation

Temporal Representation

end point

start point

SPAN Event

occurrence point

SNAPEvent

Time


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


end point

start point

SPAN Event

occurrence point

SNAPEvent

Example: X-ray performed on specific day …with associated

set of results

Time


Time

end point

start point

SPAN Event

occurrence point

SNAPEvent

Example: Period of employment as Plumber, spanning specific time-period


end point

start point

Structured SPAN Event

Time

SNAP SNAPSNAPSNAP


end point

start point


Time

SNAP SNAPSNAPSNAP

Example: History of Tumour over specific time-period …

…with set of ‘snapshots’ representing same Tumour at specific time-points


end point

start point


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

Clinical Model


Generic Model

Clinical KnowledgeService

Chronicle Model

Java Object Model

ExternalKnowledge

Sources (EKS)Ontologies,

Databases, etc.

EKS

EKSRelated

Inference

Clinical Model


Generic Model

EKSRelated

Inference


EKS

Chronicle Representation is embedded within a generic Knowledge Driven Architecture

Clinical Model

Generic Model

Generic Model


EKS

Including… SNAP/SPAN temporal representation Temporal abstraction mechanisms EKS-concept handling

Generic modelling classes…

EKSRelated

Inference

Clinical Model

Clinical Model

Generic Model


EKS

Extends generic model with clinical-specific classes

Examples… SNAPS: ProblemSnapshot, SnapClinicalProcedure, etc. SPANS: ProblemHistory, ClinicalRegime, etc.EKS

RelatedInference

Clinical Model

External Knowledge Sources (EKS)

Generic Model


EKS

Detailed (time-neutral) clinical knowledge sources

Currently: Single OWL ontologyPossibly: Multiple ontologies, databases, etc.

EKSRelated

Inference

Clinical Model

External Knowledge Sources (EKS)

Generic Model

EKSRelated

Inference


EKS

Provide… Hierarchies of concepts Sets of inter-concept relationships Sets of instance-descriptor properties attached to concepts

Clinical Model

EKS-Related Inference

Generic Model

EKSRelated

Inference


EKS

Drive… Dynamic data creation Query formulation

Currently: Description-Logic based reasonerPossibly: Rule-bases, procedural code, etc.

Arbitrarily complex inference mechanisms…

Clinical Model

EKS-Related Inference

Generic Model

EKSRelated

Inference


EKS

Note: Full EKS-related inference is neither appropriate, nor required, for (time-critical) execution of queries over thousands of patient chronicles

Clinical Model

Clinical Knowledge Service

Generic Model


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)

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)

Chronicle Model

Objects

Problem-Types

ProblemHistory

snapshots[]

ProblemSnapshot

location type

Bodily-Locations


Snapshot

Problem-Types

SPAN Event

SNAP Events

ProblemHistory

snapshots[]

ProblemSnapshot

location type

Bodily-Locations


Snapshot

External Knowledge

Sources (EKS)

Problem-Types

ProblemHistory

snapshots[]

ProblemSnapshot

location type

Bodily-Locations


Snapshot

‘type’ concept selected from

EKS

ProblemHistory

snapshots[]

ProblemSnapshot

location type

Tumour


Snapshot

Bodily-Locations

IntegerHistory

ProblemHistory

snapshots[]

ProblemSnapshot

location type

IntegerSnapshot

tumour-size

IntegerSnapshotInteger

Snapshottumour-size

Tumour


Snapshot

Bodily-Locations

‘descriptor’ variables derived

from ‘type’ concept

ProblemHistory

snapshots[]

ProblemSnapshot

location type

IntegerSnapshot

IntegerHistorytumour-size


Snapshottumour-size

Tumour

value:

time-point:

7

4/3/98


Snapshot

Bodily-Locations

Values allocated to snapshot ‘descriptors’

ProblemHistory

snapshots[]

ProblemSnapshot

location type

IntegerSnapshot



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


Snapshot

Bodily-Locations

History ‘descriptor’ values derived automatically

Breast

‘location’ concept selected from EKS

ProblemHistory

snapshots[]

ProblemSnapshot

location type

IntegerSnapshot



Snapshottumour-size

Tumour


Snapshot

her2-receptor

her2-receptor

Breast

ProblemHistory

snapshots[]

ProblemSnapshot

location type

IntegerSnapshot



Snapshottumour-size

Tumour


SnapshotBoolean

SnapshotBooleanSnapshotBoolean

Snapshot

BooleanHistory

Additional ‘descriptor’ variables inferred via

EKS-related reasoning

her2-receptor

her2-receptor

Breast

ProblemHistory

snapshots[]

ProblemSnapshot

location type

IntegerSnapshot



Snapshottumour-size

Tumour


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’

Chronicle Repositoryand

Query Engine

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


• 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)

ChroniclisationProcess

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

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

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

the clef chronicle: transforming patient records into an e-science resource jeremy rogers, colin...

Documents

chronicle representation

month period slide

query slide

clef chronicle slide

specific timeperiod

stage of stage

overview slide

temporal information