1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The World of Health IT Ontology–based Research in e-Health Vienna, Austria - October 23, 2007 Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences University at Buffalo, NY, USA http://www.org.buffalo.edu/RTU

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 2 Presentation overview 1.A bit about me 2.Some problems that can be solved using ‘ontology’ 3.What are ‘ontologies’ 4.Realism-based ontology 5.Referent Tracking 6.EHR Archetypes 7.Coping with change

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 3 Short personal history 1959 -... 1977 1989 1992 1998 2002 2004 2006

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 4 Buffalo NYC Chicago

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 5

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 6 Center of Excellence in Bioinformatics & Life Sciences Buffalo, NY

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 7

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 8 Google, November 2004

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 9 October 2007

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 10 My work: ontology-based research Realism- based Ontology What is generic Referent Tracking What is specific Instance-of

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 11 Access this presentation http://www.org.buffalo.edu/RTU/ papers/WHIT2007-Ceusters.ppt

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 1: What does ontology try to solve ?

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 13 A general belief: Better information Better care

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 14 ‘Information’ versus ‘informing’ Better information Better care Being better informed

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 15 A general belief:Being better informed Concerns primarily the delivery of information: Better information Better care Being better informed

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 16 A general belief:Being better informed Concerns primarily the delivery of information: –Timely, –Where required (e.g. bed-side computing), –What is permitted, –What is needed. Involves: –Connecting systems, –Making systems interoperable: Syntactically, Semantically.

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 17 HIMSS Integration and Interoperability Steering Committee the ability of health IS to work together within and across organizational boundaries in order to advance the effective delivery of healthcare for individuals and communities, covering the following dimensions: –Uniform movement of healthcare data, –Uniform presentation of data, –Uniform user controls, –Uniform safeguarding data security and integrity, –Uniform protection of patient confidentiality, –Uniform assurance of a common degree of system service quality. Interoperability Definition and Background. Approved by HIMSS Board of Directors., 06/09/05.

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 18 HIMSS Integration and Interoperability Steering Committee the ability of health IS to work together within and across organizational boundaries in order to advance the effective delivery of healthcare for individuals and communities, covering the following dimensions: –Uniform movement of healthcare data, –Uniform presentation of data, –Uniform user controls, –Uniform safeguarding data security and integrity, –Uniform protection of patient confidentiality, –Uniform assurance of a common degree of system service quality. No mention of information quality Interoperability Definition and Background. Approved by HIMSS Board of Directors., 06/09/05.

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 19 Ontolog-Discussion: Healthcare Informatics Landscape “The Business Value for Health IT Ontology Tools in Health Data and Information Systems: Facilitates development of open-standards, interoperable networks of health information systems and EHRs, Supports patient safety and goals to reduce medical errors in health care delivery, Promotes data quality in the electronic exchange of health information.” Marc Wine, August 25, 2005 Is about quality preservation

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 20 “Better Information” must cover … EHR PHR Various modality related databases –Lab, imaging, … Classification systems Terminologies Ontologies Textbooks Patient-specific information Medical “knowledge”

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 21 How to assess whether information is “better” ? Coverage Authority Objectivity Accuracy Timeliness Utility Understandability Seems to have received most attention thus far

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 22 Ontologies Terminologies Effectiveness of ‘semantic’ technologies Coverage Authority Objectivity Accuracy Timeliness Utility Understandability Coding & classification systems

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 23 Caveat ! There are ontologies and “ontologies” !!!

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 24 Realism-based Ontologies: reality as benchmark ! Holds only for Realism-based ontologies Coverage Authority Objectivity Accuracy Timeliness Utility Understandability

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 2: The many faces of “ontologies”

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 26 The word ‘Ontology’ has two meanings Ontology: the science of what entities exist and how they relate to each other. An ontology: a representation of some domain which –(1) is intelligible to a domain expert, and –(2) is formalized in a way that allows it to support automatic information processing.

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 27 Within the context of ‘an ontology’, the word ‘domain’ has two meanings For most computer scientists: –A representation of an agreed upon conceptualization about which man and machine can communicate using an agreed upon vocabulary For philosophical ontologists: –A representation of a portion of reality Still allowing for a variety of entities to be recognised by one school and refuted by another one

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 28 Three types of ontologies Upper level ontologies: –(should) describe the most generic structure of reality Domain ontologies: –(should) describe the portion of reality that is dealt with in some domain –Special case: reference ontologies Application ontologies: –To be used in a specific context and to support some specific application

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 29 The dispute between … “Practical engineers”: –If it works for our purposes, it is ok Good philosophers: –If it works always, it is ok, and –It can only always work if it represents the relevant portion of reality faithfully.

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 30 But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort Most “ontologies” are concept-based Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 31 But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort These views require the involvement of a cognitive entity: Most “ontologies” are concept-based

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 32 But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort These views require the involvement of a cognitive entity: This view does not presuppose cognition at all Most “ontologies” are concept-based

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 33 Concept-orientation in ontology has sad consequences Too much effort goes into the specification business –OWL, DL-reasoners, translators and convertors, syntax checkers,... Too little effort into the faithfulness of the conceptualizations towards what they represent. –Pseudo-separation of language and entities “absent nipple”, “planned act”, “prevented abortion” Many ‘ontologies’ and ontology-like systems exhibit mistakes of various sorts.

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 34 Some examples Gene Ontology – menopause part_of death * SNOMED – both uterii is_a uterus * UMLS – blood pressure is_a lab result GALEN – vomitus contains carrot *corrected in most recent version

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 35 Snomed CT (July 2007): “fractured nasal bones”

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 36 SNOMED-CT: abundance of false synonymy nose bones fracture

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 37 Coding / Classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 38 A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = = Coding / Classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 39 Snomed CT (July 2007): “fractured nasal bones”

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 40 Snomed CT (July 2007): “fractured nasal bones” Problems of multiple inheritance: – (1) “… ISA fracture of skull and facial bones” Which facial bones are not part of the skull ? If there would be non-skull facial bones, how many fractures are then required ? –(2) “… ISA fracture of mid-facial bones” Which mid-facials bones or not facial bones ? –If all, then (1) is redundant –(3) “… ISA injury of nasal bones” Are not all fractures “injuries’ and if not, why would then all nasal fractures be injuries ?

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 41 Mistakes in “ontologies”: a plurality of reasons Lack of ontology development skills: Domain experts and computer scientists are not trained in ontological analysis Many ontology building manuals and ‘example ontologies’ contain fundamental mistakes –Wine ontology, pizza ontology, … Confusing terminology Class, instance, concept, … Unwarranted faith in: Ontology authoring tools (Protégé) Ontology languages (OWL, UML, …)

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 42 Compare: ‘Death by UML Fever’ It is important to emphasize that UML itself is not the direct cause of any maladies described herein. Instead, UML is largely an innocent victim caught in the midst of poor process, no process, or sheer incompetence of its users. UML sometimes does amplify the symptoms of some fevers as the result of the often divine-like aura attached to it. For example, it is not uncommon for people to believe that no matter what task they may be engaged in, mere usage of UML somehow legitimizes their efforts or guarantees the value of the artifacts produced. Alex E. Bell. Death by UML Fever. Queue 2(1), March 2004, ACM Press, 72 – 80, 2004

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 43 Who would not be impressed ? Fig. 10: BRIDG Comprehensive Class and attribute diagram - (Logical diagram), p99

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 44 HL7-RIM Animal Definition: A subtype of Living Subject representing any animal-of-interest to the Personnel Management domain. LivingSubject Definition: A subtype of Entity representing an organism or complex animal, alive or not. Smith B, Ceusters W. HL7 RIM: An Incoherent Standard, Stud Health Technol Inform. 2006;124:133-138.

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 45 Fundamental mistake in HL7 RIM Act as statements or speech-acts are the only representation of real world facts or processes in the HL7 RIM. The truth about the real world is constructed through a combination (and arbitration) of such attributed statements only, and there is no class in the RIM whose objects represent "objective state of affairs" or "real processes" independent from attributed statements. As such, there is no distinction between an activity and its documentation. HL7 Reference Information Model V 02-14n 11/1/2006 - Basis for Normative Edition 2007 Retrieved Oct 20, 2007 from http://www.hl7.org/Library/data-model/RIM/C30214n/rim0214nc.zip

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 46 Thus: watching sports is as good as doing sports HL7 as causal factor in pandemic obesity

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 47 AdverseEvent (BRIDG logical model p168, HE!) Type: Class Assessment Status: Proposed. Version 1.0. Phase 1.0. Package: Clinical Research Activities Keywords: Detail: Created on 05/24/2006. Last modified on 01/26/2007. GUID: {CD620136-3CB9-4382-802B-F6CA82F98C10} An observation of a change in the state of a subject that is assessed as being untoward by one or more interested parties within the context of protocol-driven research or public health.

48. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 48 Being critical ≠ being negative RFQ-NCI-60001-NG: Review of NCI Thesaurus and Development of Plan to Achieve OBO-Compliance Grant to Apelon (H. Solbrig) to improve NCIT

49. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 49 Will HL7 RIM become better ? cc August 31, 2007 To: HL7 Co-chairs FR: Chuck Meyer, Chair, HL7 Board of Directors RE: Version 3 Editing project Dear Co-chairs: The HL7 Board of Directors has identified the need to review the existing V3 portfolio of standards for clarity, consistency, and ease of use. To that end, it has approved a renewal of contract to review existing documentation, identify issues, and propose tactics for making the V3 standard and supporting materials easier to understand and use.

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 50 Will ontologies in general become better ? There is hope !

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 51

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 52 Will ontologies in general become better ? There is hope ! Open Biomedical Ontology Foundry - Principles –All Foundry terms should be defined using terms and relations drawn from other Foundry ontologies; –All relations are to be drawn from the OBO Relation Ontology; –Single inheritance; –All terms should refer to types (or defined classes) which have instances; –Use of Basic Formal Ontology.

53. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 3: Basic Formal Ontology

54. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 54 Basic Formal Ontology An ontology which is –Realist: –Fallibilist: –Perspectivalist: –Adequatist: There is only one reality and its constituents exist independently of our (linguistic, conceptual, theoretical, cultural) representations thereof, theories and classifications can be subject to revision, there exists a plurality of alternative, equally legitimate perspectives on that one reality these alternative views are not reducible to any single basic view.

55. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 55 Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

56. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 56 Reality exist before any observation R

57. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 57 Reality exist before any observation Humans had a brain well before they knew they had one. Trees were green before humans started to use the word “green”. R And also most structures in reality are there in advance.

58. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 58 Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up ‘in their minds’ cognitive representations of the world; Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

59. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 59 The ontology author acknowledges the existence of some Portion Of Reality (POR) R B

60. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 60 R B Some portions of reality escape his attention.

61. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 61 Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up ‘in their minds’ cognitive representations of the world; 3.To make these representations publicly accessible in some enduring fashion, they create representational artifacts that are fixed in some medium. Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

62. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 62 R He represents only what he considers relevant O B #1 RU 1 B1 RU 1 O1 Both RU 1 B1 and RU 1 O1 are representational units referring to #1; RU 1 O1 is NOT a representation of RU 1 B1 ; RU 1 O1 is created through concretization of RU 1 B1 in some medium.

63. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 63 But please be aware... These concretizations are NOT supposed to be the representations of these cognitive representations; They are representations of the reality (probably containing mistakes) “concept representation” We should not be in the business of

64. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 64 Some characteristics of an optimal ontology Each representational unit in such an ontology would designate –(1) a single portion of reality (POR), which is –(2) relevant to the purposes of the ontology and such that –(3) the authors of the ontology intended to use this unit to designate this POR, and –(4) there would be no PORs objectively relevant to these purposes that are not referred to in the ontology.

65. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 65 Basic components of a realist view of the world The world consists of –entities that are Either particulars or universals; Either occurrents or continuants; Either dependent or independent; and, –relationships between these entities of the form e.g. is-instance-of, lacks e.g. is-member-of, is-part-of e.g. isa (is-subtype-of) Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

66. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 66 The example to work (partially) out: ‘walking’ methis walking Has-participant at t 2 human being Instance-of at t living creature Is_a walking Instance-of my left leg part-of at t this leg moving leg moving part-of leg to make me walk function process Instance-of at t Instance-of at t Is_a Instance-of Has- Participant at t Is-realized- In at t Has-function at t

67. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Basic entities in realism-based ontology: three main distinctions 123

68. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 68 Particulars methis walking my left leg this leg moving to make me walk Individual entities that carry identity and preserve their identity over time 1

69. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 69 Universals human being living creature walkingleg moving leg function process Entities which exist “in” the particulars amongst which there is a relation of similarity not found with other particulars 1

70. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 70 Particulars and Universals methis walking my left leg this leg moving to make me walk human being living creature walkingleg moving leg function process Instance-of at t Instance-of at t Instance-of at t Instance-of 1

71. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 71 Continuants and Occurrents methis walking my left leg this leg moving to make me walk human being living creature walkingleg moving leg function process Instance-of at t Instance-of at t Instance-of at t Instance-of 2

72. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 72 Continuants me human being Instance-of at t my left leg leg to make me walk function Instance-of at t Instance-of at t Continuants are entities which endure (=continue to exist) while undergoing different sorts of changes, including changes of place. While they exist, they exist “in total”. 2

73. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 73 Continuants preserve identity while changing caterpillarbutterfly animal t human being living creature me child Instance-of in 1960 adult me Instance-of since 1980 2

74. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 74 Occurrents this walking walking Instance-of this leg moving leg moving Instance-of Occurrents are changes. Occurrents unfold themselves during temporal phases. At any point in time, they exist only in part. 2

75. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 75 Independent versus dependent methis walking human being Instance-of at t living creature Is_a walking Instance-of my left leg this leg moving leg moving leg to make me walk function process Instance-of at t Instance-of at t Is_a Instance-of 3

76. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 76 Independent versus dependent Independent entities Do not require any other entity to exist to enable their own existence Dependent entities Require the existence of another entity for their existence methis walking my left leg this leg moving to make me walk 3

77. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 77 Independent versus dependent Independent entities Do not require any other entity to exist to enable their own existence Dependent entities Require the existence of another entity for their existence methis walking my left leg this leg moving to make me walk Independent continuants Dependent continuants Occurrents (are all dependent) 3

78. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 78 Dependent continuants Realized –Quality:redness (of blood) Realizable –Function:to flex (of knee joint) –Role:student –Power:boss –Disposition:brittleness (of a bone) 3

79. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 79 Dependent continuants Realized –Quality:redness (of blood) Realizable –Function:to flex (of knee joint) –Role:student –Power:boss –Disposition:brittleness (of a bone) Realizations flexing studying ordering breaking continuantsoccurrents 3

80. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relations in realism-based ontology Smith B, Ceusters W, Klagges B, Koehler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector A, Rosse C. Relations in biomedical ontologies, Genome Biology 2005, 6:R46.Relations in biomedical ontologies

81. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 81 Basic sorts of relationships universal particular ?

82. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 82 Universals and classes universal PPPP PPPP PPPP instance-of member-of extention-of Defined class

83. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 83 General principle about relationships All universal level relationships are defined on the basis of particular level relationships

84. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 84 Primitive instance-level relationships (RO) c instance_of C at t - a primitive relation between a continuant instance and a class which it instantiates at a specific time p instance_of P - a primitive relation between a process instance and a class which it instantiates holding independently of time c part_of c1 at t - a primitive relation between two continuant instances and a time at which the one is part of the other p part_of p1, r part_of r1 - a primitive relation of parthood, holding independently of time, either between process instances (one a subprocess of the other), or between spatial regions (one a subregion of the other) c located_in r at t - a primitive relation between a continuant instance, a spatial region which it occupies, and a time r adjacent_to r1 - a primitive relation of proximity between two disjoint continuants t earlier t1 - a primitive relation between two times c derives_from c1 - a primitive relation involving two distinct material continuants c and c1 p has_participant c at t - a primitive relation between a process, a continuant, and a time p has_agent c at t - a primitive relation between a process, a continuant and a time at which the continuant is causally active in the process

85. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 85 Is_a is defined over instance-of (1) For continuants C is_a C1 = [definition] for all c, t, if c instance_of C at t then c instance_of C1 at t. For occurrents P is_a P1 = [definition] for all p, if p instance_of P then p instance_of P1.

86. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 86 Is_a is defined over instance-of (2) human being living creature me universals particulars is_a instance-of at t therefore

87. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 87 Is_a is defined over instance-of (3) childadultcaterpillarbutterfly human being living creature animal me More than subset or inclusion ! is_a Instance-of t1t2

88. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 88 Transformation Derivation continuation fusion fission

89. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 89 Part-of different for continuants and occurrents methis walking human being Instance-of at t living creature Is_a walking Instance-of my left leg this leg moving leg moving leg process Instance-of at t Is_a Instance-of part-of at t part-of

90. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 90 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left leg part-of at t leg Instance-of at t C part_of C1 = [def] for all c, t, if Cct then there is some c1 such that C1c1t and c part_of c1 at t. Cct = c instance-of C at t

91. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 91 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left leg part-of at t leg Instance-of at t C part_of C1 = [def] for all c, t, if Cct then there is some c1 such that C1c1t and c part_of c1 at t. Cct = c instance-of C at t Part-of ?

92. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 92 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left leg part-of at t leg Instance-of at t Horse legs are not parts of human beings Amputated legs are not parts of human beings ‘Canonical leg is part of canonical human being’, but…, there are (very likely) no such particulars … Part-of ?

93. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 93 Are we done ? Is an accurate coding system, classification system, terminology, ontology, …, a necessary and sufficient condition for obtaining “better” information ? Necessary: yes ! Sufficient: no !

94. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 94 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateObsCodeNarrative 093920/12/1998255087006malignant polyp of biliary tract Three references of hypertension for the same patient denote three times the same disease. If two different fracture codes are used in relation to observations made on the same day for the same patient, they might refer to the same fracture The same type of location code used in relation to three different events might or might not refer to the same location. If the same fracture code is used for the same patient on different dates, then these codes might or might not refer to the same fracture. The same fracture code used in relation to two different patients can not refer to the same fracure. If two different tumor codes are used in relation to observations made on different dates for the same patient, they may still refer to the same tumor. Using codes does not prevent ambiguities as to what is described: how many disorders are listed?

95. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 95 Consequences Very difficult to: –Count the number of (numerically) different diseases Bad statistics on incidence, prevalence,... Bad basis for health cost containment –Relate (numerically the same or different) causal factors to disorders: –Dangerous public places (specific work floors, swimming pools), –dogs with rabies, –HIV contaminated blood from donors, –food from unhygienic source,... Hampers prevention –...

96. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 4: Referent Tracking

97. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 97 Purpose: –explicit reference to the concrete individual entities relevant to the accurate description of each patient’s condition, therapies, outcomes,... Proposed Solution: Referent Tracking Now! That should clear up a few things around here ! Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

98. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 98 Proposed Solution: Referent Tracking Numbers instead of words! Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78. Method: –Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity 78

99. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 99 ‘John Doe’s ‘John Smith’s liver tumor was treated with RPCI’s irradiation device’ ‘John Doe’s liver tumor was treated with RPCI’s irradiation device’ The principle of Referent Tracking #1 #3 #2 #4 #5 #6 treating person liver tumor clinic device instance-of at t 1 #10 #30 #20 #40 #5 #6 inst-of at t 2 inst-of at t 2 inst-of at t 2 inst-of at t 2 inst-of at t 2

100. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 100 EHR – Ontology “collaboration”

101. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 101 Advantage: better reality representation 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateObsCodeNarrative 093920/12/1998255087006malignant polyp of biliary tract IUI-001 IUI-003 IUI-004 IUI-005 IUI-007 IUI-002 IUI-012 IUI-006 7 distinct disorders

102. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 102 Essentials of Referent Tracking Generation of universally unique identifiers; deciding what particulars should receive a IUI; finding out whether or not a particular has already been assigned a IUI (each particular should receive maximally one IUI); using IUIs in the EHR, i.e. issues concerning the syntax and semantics of statements containing IUIs; determining the truth values of statements in which IUIs are used; correcting errors in the assignment of IUIs.

103. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 103 PtoU statements – particular to universal U i = IUI a is the IUI of the author of the statement, t a a reference to the time when the statement is made, inst a reference to an instance relationship available in o obtaining between p and cl, o a reference to the ontology from which inst and u are taken, IUI p the IUI referring to the particular whose inst relationship with u is asserted, u the universal in o to which p enjoys the inst relationship, and, t r a reference to the time at which the relationship obtains.

104. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 104 Architecture of a Referent Tracking System (RTS) RTS: system in which all statements referring to particulars contain the IUIs for those particulars judged to be relevant. Ideally set up as broad as possible: Services: –IUI generator –IUI repository: statements about assignments and reservations –Referent Tracking ‘Database’ (RTDB): index (LSID) to statements relating instances to instances and classes Manzoor S, Ceusters W, Rudnicki R. Implementation of a Referent Tracking System. International Journal of Healthcare Information Systems and Informatics 2007;2(4):41-58.

105. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 105 Management of the IUI-repository Adequate safety and security provisions –Access authorisation, control, read/write,... –Pseudonymisation Deletionless but facilities for correcting mistakes. Registration of assertion ASAP after IUI assignment (virtual, e.g. LSID) central management with adequate search facilities.

106. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 106 Referent Tracking System

107. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 107 Are we now done ? We have thus far discussed: –An accurate way to represent what is generic by means of ontologies, –An accurate way to represent what is specific by means of referent tracking. We did not discuss what is sensible or useful to represent IN electronic health records. EHR Archetypes

108. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 5: EHR Archetypes as pragmatic windows on reality

109. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 109 EHR Archetypes An EHR archetype is an agreed, formal and interoperable specification of the data and their inter-relationships that must or may be logically persisted within an electronic health record for documenting a particular clinical observation, evaluation, instruction or action. Dipak Kalra. Archetypes: the missing link? EuroRec Annual Conference 2006 EHR systems and certification

110. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 110 13606 Parts 1 & 2: EHR Extracts and archetypes System A SenderSystem B Receiver EN 13606 Common Archetype Reference Model conforms to TRANSFORM Merge Extract EHR Extract Data Capture using EHR-A EHR-B Archetype Library based on selected from Interpretation points to Query uses FORM TRANSFORM Care Pathway Archetypes using Archetypes Dipak Kalra. Archetypes: the missing link? EuroRec Annual Conference 2006 EHR systems and certification

111. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 111 A message to mapmakers ‘highways are not painted red, rivers don't have county lines running down the middle, and you can't see contour lines on a mountain.’ William Kent, Data and Reality. First published by North Holland in 1978. Republished in 1998 by 1stBooks.

112. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 112 THOU SHALL NOT CONFUSE … information representation with domain representation data are about observables, but are not observables Information about X part_of information about Y X part of Y

113. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 113 (part of) OpenEHR ‘Diagnosis’ archetype http://svn.openehr.org/knowledge/archetyp es/dev/html/en/openEHR-EHR- EVALUATION.problem-diagnosis.v1.html A problem, condition or issue defined by a clinician which is deemed summative of a range of symptoms or concerns of the person and a useful label of these.

114. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 114 How to obtain high quality archetypes ? As for building high quality ontologies: –Good tools and representation languages are a necessary but not a sufficient condition; –More important: skills in the right sort of modeling; –No skills in the right sort of modeling of there is no skill in performing a sound ontological analysis. Some recommendations: –Avoid cancerous and metastatic growth of archetypes induced by enthusiasm; –Understand that the CEN standard’s specifications are about the format, but not the relation with reality; –Apply OBO Foundry principles.

115. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 115 A realism-based perspective on ‘diagnosis’ instance-of at t #105 caused by

116. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A ‘diagnosis’ as a proposition Any configuration of representational units which is believed to mirror the portion of reality consisting of an organism’s disease and the relationships this disease enjoys with the entities that caused the disease or influence its course, whereby some part of this configuration of representational units refers to the universal of which that disease is believed to be an instance, or the defined class of which it is believed to be a member.

117. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 117 A well-formed diagnosis of ‘pneumococal pneumonia’ A configuration of representational units; Believed to mirror the person’s disease; Believed to mirror the disease’s cause; Refers to the universal of which the person’s disease is believed to be an instance. #56 John’s Pneumonia #78 John’s portion of pneumococs Pneumococcal pneumonia caused by Instance-of at t1 Disease isa

118. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 118 The same diagnosis can be expressed in various forms #56#78 Pneumococcal pneumonia caused by Instance-of at t1 #56#78 Pneumonia caused by Portion of pneumococs Instance-of at t1 Disease isa caused by

119. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 6: Coping with change

120. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 120 Accept that everything may change: 1.changes in the underlying reality: Particulars and universals come and go 2.changes in our (scientific) understanding: The planet Vulcan does not exist 3.reassessments of what is considered to be relevant for inclusion (notion of purpose). 4.encoding mistakes introduced during data entry or ontology development.

121. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 121 Key requirement for updating Any change in an ontology or EHR should be associated with the reason for that change to be able to assess later what kind of mistake has been made !

122. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 122 Example: a person (in this room) ’s gender in the EHR In John Smith’s EHR: –At t 1 : “male”at t 2 : “female” What are the possibilities ? Change in reality: transgender surgery change in legal self-identification Change in understanding: it was female from the very beginning but interpreted wrongly Correction of data entry mistake: it was understood as male, but wrongly transcribed

123. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 123 Reality versus belief, both in evolution t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 p4: an artifact on an image p5: a coin lesion that really corresponds with John’s tumor (IUI-#5): acknowledgement of the referential nature of p5

124. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 124 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 In the beginning, there was nothing …

125. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 125 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 Evolution (or some creative designer) brings benign tumors to existence, but we, poor humans, don’t know that yet…

126. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 126 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 The existence of benign tumors is acknowledged, but malignancy not yet …

127. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 127 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 We know about malignancy, a growth in John Doe, benign, came about, but we are not aware of it. Malignancy has been discovered.

128. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 128 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John consulted a physician, a picture is taken, it shows in reality a lesion, but it is not perceived.

129. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 129 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John’s tumor is being discovered, but that it turned malignant, remains unnoticed.

130. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 130 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 A second image is taken, that image shows a lesion that is correctly perceived, and allows to make the diagnosis of malignancy.

131. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 131 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John’s tumor is treated by means of RPCI’s irradation device, and wrongly believed to have disappeared.

132. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 132 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John is lucky: his tumor indeed disappeared. His physician is lucky: he escapes a maltreatment suite.

133. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 133 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 The government decides that malignancy doesn’t exist anymore: a convenient way to save on reimbursement and law suites.

134. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 134 Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

135. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 135 Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

136. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 136 Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

137. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 137 Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

138. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 138 Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

139. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 139 Valid presence Valid absence

140. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 140 Unjustified presence Unjustified absence

141. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 141 Updating is an active process authors assume in good faith that –all included representational units are of the P+1 type, and –all they are aware of, but not included, of A+1 or A+2. If they become aware of a mistake, they make a change under the assumption that their changes are also towards the P+1, A+1, or A+2 cases. Thus at that time, they know of what type the previous entry must of have been under the belief what the current one is, and the reason for the change.

142. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 142 This leads to a calculus … NOT: –to demonstrate how good an individual version of an ontology or (specific) patient record is, But rather to measure –how much it improved (hopefully) as compared to its predecessors. –how the authors are improving. Principle: recursive belief revision

143. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusion

144. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 144 Did we cover our dimensions for “better information”? Coverage Authority Objectivity Accuracy Timeliness Utility Understandability Realism-based Ontology Referent Tracking EHR Archetypes Referent-based Change management

145. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 145 Conclusion (1) Building high quality ontologies is hard. Not everybody has the right skills –Experts in driving cars are not necessarily experts in car mechanics (and the other way round). Ontologies should represent the state of the art in a domain, i.e. the science. –Science is not a matter of consensus or democracy (cfr HL7 RIM problem). Natural language relates more to how humans talk about reality or perceive it, than to how reality is structured. No high quality ontology without the involvement of ontologists.

146. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 146 Conclusion (2) Realist ontology is a powerful QA tool for building high quality ontologies AND high quality databases; Referent tracking, based on realist ontology, is a means to remove the ambiguity in data that cannot be solved by realist ontology alone; –It is a form of “adult” annotation Application of RT requires a globally accessible repository –Adds another level to interoperability. The use of “meaningless” IUIs allows very strict safety and security measures to be implemented.

147. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 147 Goal: new form 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. –Data ‘eternally’ reusable independent of the purpose for which they have been generated.