1. 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

2. 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

3. 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

4. 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 External Knowledge Sources (EKS)

5. Properties from External Knowledge 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…

6. 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 Relationships Between EKS Concepts

7. 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

8. 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

9. 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

10. Chronicle System: Overview

11. (1) Chronicle Representation Chronicle Representation 1

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

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

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

15. (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) 1 3 2 4 5

16. Chronicle Representation

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

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

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

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

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

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

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

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

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

26. Clinical Model Generic Model Clinical Knowledge Service EKS Including… SNAP/SPAN temporal representation Temporal abstraction mechanisms EKS-concept handling Generic modelling classes… EKS Related Inference

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

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

29. Clinical Model External Knowledge Sources (EKS) Generic Model EKS Related Inference Clinical Knowledge Service EKS Provide… Hierarchies of concepts Sets of inter-concept relationships Sets of instance- descriptor properties attached to concepts

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

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

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

33. Problem- Types Problem History snapshots[] Problem Snapshot locationtype Bodily- Locations Problem Snapshot Problem Snapshot 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)

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

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

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

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

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

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

40. Problem History snapshots[] Problem Snapshot locationtype Integer Snapshot Integer History tumour-size Integer Snapshot Integer Snapshot tumour-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 Problem Snapshot Problem Snapshot Bodily- Locations History descriptor values derived automatically

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

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

43. her2-receptor Breast Problem History snapshots[] Problem Snapshot locationtype Integer Snapshot Integer History tumour-size Integer Snapshot Integer Snapshot tumour-size Tumour Problem Snapshot Problem Snapshot Boolean Snapshot Boolean Snapshot Boolean Snapshot Boolean History 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 63.72 36.28 value: time-point: false 4/3/98 Values allocated/derived for new descriptors

44. Chronicle Repository and Query 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. Chroniclisation Process

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