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CLEF: Clinical E-Science Framework

The CLEF Chronicle: Transforming Patient Records into an E-Science Resource Jeremy Rogers, Colin Puleston, Alan Rector James Cunningham, Bill Wheeldin, Jay Kola Bio-Health Informatics Group Department of Computer Science University of Manchester. CLEF: Clinical E-Science Framework.

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CLEF: Clinical E-Science Framework

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  1. The CLEF Chronicle: Transforming Patient Records into an E-Science ResourceJeremy Rogers, Colin Puleston, Alan RectorJames Cunningham, Bill Wheeldin, Jay KolaBio-Health Informatics GroupDepartment of Computer ScienceUniversity of Manchester

  2. CLEF: Clinical E-Science Framework • Improving the storage and processing of Electronic Health Records to enhancegeneralclinical care • Supporting clinical research via the creation of a clinical research repository,known as the CLEF Chronicle

  3. Chronicle Query WHAT PERCENTAGE OF PATIENTS WHO… FIRST: Had cancer with stage of stage-2 …located somewhere in the leg …with primary tumour …that doubled in size within a 3 month period THEN: Underwent surgical-intervention to remove all tumours ALSO… THEN: Survived for at least ten years …whilst remaining in remission for the full extent of this period

  4. Concepts from ExternalKnowledge Sources (EKS) WHAT PERCENTAGE OF PATIENTS WHO… FIRST: Had cancer with stage of stage-2 …located somewhere in the leg …with primary tumour …that doubled in size within a 3 month period THEN: Underwent surgical-intervention to remove all tumours ALSO… THEN: Survived for at least ten years …whilst remaining in remission for the full extent of this period

  5. Properties from ExternalKnowledge Sources (EKS) WHAT PERCENTAGE OF PATIENTS WHO… FIRST: Had cancer with stage of stage-2 …located somewhere in the leg …with primary tumour …that doubled in size within a 3 month period THEN: Underwent surgical-intervention to remove all tumours ALSO… THEN: Survived for at least ten years …whilst remaining in remission for the full extent of this period

  6. Implicit RelationshipsBetween EKS Concepts WHAT PERCENTAGE OF PATIENTS WHO… FIRST: Had cancer with stage of stage-2 …located somewhere in the leg …with primary tumour …that doubled in size within a 3 month period shinpart-oflower-leg part-ofleg THEN: mastectomyis-asurgical-intervention Underwent surgical-intervention to remove all tumours ALSO… THEN: Survived for at least ten years …whilst remaining in remission for the full extent of this period

  7. Temporal Information WHAT PERCENTAGE OF PATIENTS WHO… FIRST: Had cancer with stage of stage-2 …located somewhere in the leg …with primary tumour …that doubled in size within a 3 month period THEN: Underwent surgical-intervention to remove all tumours ALSO… THEN: Survived for at least ten years …whilst remaining in remission for the full extent of this period

  8. ARBITRARY TEMPORAL SEQUENCES WHAT PERCENTAGE OF PATIENTS WHO… FIRST: Had cancer with stage of stage-2 …located somewhere in the leg …with primary tumour …that doubled in size within a 3 month period THEN: Underwent surgical-intervention to remove all tumours ALSO… THEN: Survived for at least ten years …whilst remaining in remission for the full extent of this period

  9. Temporal Abstractions WHAT PERCENTAGE OF PATIENTS WHO… FIRST: Had cancer with stage of stage-2 …located somewhere in the leg …with primary tumour …that doubled in size within a 3 month period …that doubled in size within a 3 month period THEN: Underwent surgical-intervention to remove all tumours ALSO… THEN: Survived for at least ten years …whilst remaining in remission for the full extent of this period …whilst remaining in remission for the full extent of this period

  10. Chronicle System: Overview

  11. (1) Chronicle Representation 1 Chronicle Representation

  12. (2) Chronicle Repository + Query Engine 1 Chronicle Representation Query Engine 2 Chronicle Repository

  13. (3) ‘Chroniclisation’ Process 3 1 Chronicle Representation Chronicliser Text Processor (Sheffield) Query Engine 2 Chronicle Repository EHR Repository (UCL)

  14. (4) Chronicle Simulator 3 1 Chronicle Representation Chronicliser Chronicle Simulator Text Processor (Sheffield) Query Engine 4 2 Chronicle Repository EHR Repository (UCL)

  15. (5) Browser + Query GUIs 5 Simple Browser + Query Formulator Query Formulator (Open University) 3 1 Chronicle Representation Chronicliser Chronicle Simulator Text Processor (Sheffield) Query Engine 4 2 Chronicle Repository EHR Repository (UCL)

  16. ChronicleRepresentation

  17. Temporal Representation SNAP Event SPAN Event start point end point occurrence point Time

  18. Temporal Representation Note:For the Patient Chronicle the atomic time-unit equals one-day… …hence, for example, Surgical-Operations andConsultationsare SNAP Events SNAP Event SPAN Event end point start point occurrence point Time

  19. Temporal Representation Example:X-ray performed on specific day …with associated set of results SNAP Event SPAN Event end point start point occurrence point Time

  20. Temporal Representation Example:Period of employment as Plumber, spanning specific time-period SNAP Event SPAN Event end point start point occurrence point Time

  21. Temporal Representation SNAP SNAP SNAP SNAP Structured SPAN Event start point end point Time

  22. Temporal Representation …with set of ‘snapshots’ representing same Tumourat specific time-points Example:History of Tumour over specific time-period… SNAP SNAP SNAP SNAP Structured SPAN Event start point end point Time

  23. Temporal Representation …whilst SPAN has set of ‘temporal-abstractions’ (e.g. max, min, etc.) summarising the tumour-size attribute Example cont.:Each SNAP has associated value for tumour-size attribute… SNAP SNAP SNAP SNAP Structured SPAN Event start point end point Time

  24. Chronicle Representation Chronicle Model Java Object Model Clinical Model Generic Model Clinical Knowledge Service External Knowledge Sources (EKS) Ontologies, Databases, etc. EKS EKS Related Inference

  25. Chronicle Representation Clinical Model Generic Model Chronicle Representation is embedded within a genericKnowledge Driven Architecture Clinical Knowledge Service EKS EKS Related Inference

  26. Generic Model Generic modelling classes… Clinical Model Generic Model Clinical Knowledge Service • Including… • SNAP/SPANtemporal representation • Temporal abstractionmechanisms • EKS-concepthandling EKS EKS Related Inference

  27. Clinical Model Extends generic model with clinical-specific classes Clinical Model Generic Model Clinical Knowledge Service Examples…  SNAPS:ProblemSnapshot, SnapClinicalProcedure, etc.  SPANS:ProblemHistory, ClinicalRegime, etc. EKS EKS Related Inference

  28. External Knowledge Sources (EKS) Clinical Model Detailed (time-neutral) clinical knowledge sources Generic Model Clinical Knowledge Service Currently: Single OWL ontology Possibly: Multiple ontologies, databases, etc. EKS EKS Related Inference

  29. External Knowledge Sources (EKS) Clinical Model Generic Model Provide…  Hierarchies ofconcepts  Sets of inter-conceptrelationships  Sets ofinstance-descriptorproperties attached to concepts Clinical Knowledge Service EKS EKS Related Inference

  30. EKS-Related Inference Arbitrarily complex inference mechanisms… Clinical Model Generic Model Drive…  Dynamic data creation  Query formulation Clinical Knowledge Service EKS Currently:Description-Logic based reasoner Possibly: Rule-bases, procedural code, etc. EKS Related Inference

  31. EKS-Related Inference Clinical Model Generic Model Note:Full EKS-related inference is neither appropriate, nor required, for (time-critical) execution of queries over thousands of patient chronicles Clinical Knowledge Service EKS EKS Related Inference

  32. Clinical Knowledge Service • Provides transparent access to… • External knowledge sources • EKS-related inference Clinical Model Generic Model Clinical Knowledge Service Simple interface… Takes:Instance of concept X, including set of descriptor values Returns:Updated descriptor-set for X (including updated constraints) EKS EKS Related Inference

  33. Bodily-Locations Problem-Types Chronicle Representation: Example Representation 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) location type Problem History snapshots[] Problem Snapshot Problem Snapshot Problem Snapshot

  34. Bodily-Locations Problem-Types location type Problem History Chronicle Model Objects snapshots[] Problem Snapshot Problem Snapshot Problem Snapshot

  35. Bodily-Locations Problem-Types SPAN Event location type Problem History SNAP Events snapshots[] Problem Snapshot Problem Snapshot Problem Snapshot

  36. Bodily-Locations Problem-Types External Knowledge Sources (EKS) location type Problem History snapshots[] Problem Snapshot Problem Snapshot Problem Snapshot

  37. Bodily-Locations Tumour ‘type’ concept selected from EKS location type Problem History snapshots[] Problem Snapshot Problem Snapshot Problem Snapshot

  38. ‘descriptor’ variables derived from ‘type’ concept Bodily-Locations Tumour Integer History location type tumour-size Problem History snapshots[] Integer Snapshot Integer Snapshot Integer Snapshot tumour-size Problem Snapshot Problem Snapshot Problem Snapshot

  39. Values allocated to snapshot ‘descriptors’ Bodily-Locations Tumour Integer History location type tumour-size Problem History snapshots[] Integer Snapshot Integer Snapshot Integer Snapshot time-point: 4/3/98 tumour-size value: 7 Problem Snapshot Problem Snapshot Problem Snapshot

  40. History ‘descriptor’ values derived automatically Bodily-Locations Tumour start-point: 4/3/98 Integer History end-point: 7/2/02 location type tumour-size start-value: 7 end-value: 43 Problem History Temporal Abstractions minimum: 7 maximum: 82 range: 75 snapshots[] Integer Snapshot increase-rate: 0.051 Integer Snapshot Integer Snapshot tumour-size Problem Snapshot Problem Snapshot Problem Snapshot

  41. ‘location’ concept selected from EKS Breast Tumour Integer History location type tumour-size Problem History snapshots[] Integer Snapshot Integer Snapshot Integer Snapshot tumour-size Problem Snapshot Problem Snapshot Problem Snapshot

  42. Additional ‘descriptor’ variables inferred via EKS-related reasoning Breast Tumour Integer History location type tumour-size Problem History Boolean History her2-receptor snapshots[] Integer Snapshot Integer Snapshot Integer Snapshot tumour-size Problem Snapshot Problem Snapshot Problem Snapshot Boolean Snapshot Boolean Snapshot Boolean Snapshot her2-receptor

  43. Values allocated/derived for new ‘descriptors’ Breast Tumour start-point: 4/3/98 Integer History end-point: 7/2/02 location type tumour-size start-value: false end-value: true Problem History Boolean History always-true: false her2-receptor always-false: false percent-true: 63.72 snapshots[] Integer Snapshot percent-false: 36.28 Integer Snapshot Integer Snapshot tumour-size Problem Snapshot Problem Snapshot Problem Snapshot Boolean Snapshot Boolean Snapshot time-point: 4/3/98 Boolean Snapshot her2-receptor value: false

  44. Chronicle RepositoryandQuery Engine

  45. 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

  46. 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)

  47. ChroniclisationProcess

  48. 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

  49. 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

  50. 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

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