1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U RAMIT VZW - Gent, Belgium - 2009, Jan 5 Introduction to Realism-based Ontology Development Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences, and National Center for Biomedical Ontology, University at Buffalo, NY, USA

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Tutorial overview Theories: –Ontology and Terminology –Fundamentals of Philosophical Realism Basic Formal Ontology –Reality and Representations –Definitions: what is there to ‘define’ ? –Relations and the structure of reality –On the practical importance of the particular / universal distinction Applications: –‘Adverse event’ in ReMINE –Ontology development methodology –EHR Archetypes and Ontology –(mapping and annotation) –(reasoning)

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology and Terminology

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Ontology’: one word, two meanings In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; In computer science and (biomedical informatics) applications: –An ontology (plural: ontologies) is a shared and agreed upon conceptualization of a domain;

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U However … Although (almost) everybody (knowledgeable) agrees that –an ontology is a representation, there is a huge variety in –what the representational units in an ontology stand for, if anything at all, –the degree to which the structure of the ontology corresponds with the structure of that part of reality it intends to represent.

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Three major views on reality Basic questions: –What does a general term such as ‘diabetes’ denote? –Do generic things exist? yes: in particulars Universal perhaps: in minds Concept no Collection of particulars RealismConceptualismNominalism

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Dominant view in computer science is conceptualism Basic questions: –What does a general term such as ‘diabetes’ denote? –Do generic things exist? yes: in particulars perhaps: in minds no UniversalConceptCollection of particulars RealismConceptualismNominalism

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Dominant view in computer science is conceptualism RealismConceptualismNominalism Semantic Triangle concept objectterm Embedded in Terminology

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relation between language and reality ‘dog’ ‘hond’ ‘chien’ Things in realityTerms in language expressed by denotes

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relation between language and reality ‘dog’ ‘hond’ ‘chien’ Things in realityTerms in language

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminology basics: the semantic triangle ‘dog’ ‘Concept’ ‘hond’ ‘chien’

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Terminology’: one word, two meanings Terminology is the study of identifying and labelling ‘concepts’ pertaining to a subject field. Terminology related activities: –analysing the concepts and concept structures, –identifying the terms assigned to the concepts, –establishing correspondences between terms, possibly in various languages, –compiling a terminology, on paper or in databases, –managing terminology databases, –creating new terms, as required.

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U This is perfect terminology Unicorn ‘unicorn’

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Fundamental problem of the semantic triangle concepts termsobjects What is a concept? –A unit of thought? Thus person specific –A unit of knowledge? What is knowledge? –That what we know? »Where is the that? –The meaning of a term? Often: ‘the meaning of a concept’

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = = One example of what this confusion causes

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Snomed CT (July 2007): “fractured nasal bones” 2

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = = One example of what this confusion causes A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology / Terminology divide Terminology: –solves certain issues related to language use, i.e. with respect to how we talk about entities in reality (if any); Relations between terms / concepts –does not provide an adequate means to represent independent of use what we talk about, i.e. how reality is structured; Women, Fire and Dangerous Things (Lakoff). Prevented abortions, absent nipples, unicorns Ontology (of the right sort) : –Language and perception neutral view on reality. Relations between entities in first-order reality

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U An alternative: philosophical realism

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Ontology’: one word, two meanings In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; In computer science and (biomedical informatics) applications: –An ontology (plural: ontologies) is a shared and agreed upon conceptualization of a domain; Our ‘realist’ view within the Ontology Research Group combines the two: –We use realism, a specific theory of ontology, as the basis for building high quality ontologies, using reality as benchmark.

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Realism-based ontology Basic assumptions: 1.reality exists objectively in itself, i.e. independent of the perceptions or beliefs of cognitive beings; 2.reality, including its structure, is accessible to us, and can be discovered through (scientific) research; 3.the quality of an ontology is at least determined by the accuracy with which its structure mimics the pre-existing structure of reality.

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Realism-based ontology Three levels of reality: 1.First-order reality: what is on the side of persons, organizations, … 2.Cognitive representations: what cognitive agents assume to observe and know ‘in their mind’ 3.Representational artefacts for communication, documentation, … Terms, definitions, drawings, images, … Assumption: The quality of an ontology is at least determined by the accuracy with which its structure mimics the pre-existing structure of reality. 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

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Compare with Alberti’s grid reality representation Ontological theory

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

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

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality exist before any observation R And also most structures in reality are there in advance.

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

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

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

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

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

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 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, e.g. is-member-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

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Continuants (aka endurants) –have continuous existence in time –preserve their identity through change –exist in toto whenever they exist at all Occurrents (aka processes) –have temporal parts –unfold themselves in successive phases –exist only in their phases Continuants versus Occurrents

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U You are a continuant Your life is an occurrent You are 3-dimensional Your life is 4-dimensional

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Dependent entities require independent continuants as their bearers There is no run without a runner There is no smile without a face

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Dependent vs. independent continuants Independent continuants (persons, medical devices, buildings) Dependent continuants –qualities : sharp, red –shapes : round, square –roles: doctor, patient –propensities: breakable –functions: to pump blood

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U All occurrents are dependent entities They are dependent on those independent continuants which are their participants (agents, patients, media...) –Stabbing –Punching –Running

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Basic Formal Ontology’s (BFO) upper levels BFO:Entity BFO:Occurrent BFO:Processual entity BFO:Process BFO:Temporal region BFO:Scattered temporal region BFO:Continuant BFO:Independent continuant BFO:Material entity BFO:Object BFO:Dependent Continuant BFO:Specifically Dependent Continuant BFO:Quality BFO:Realizable entity BFO:Disposition BFO:Site BFO:Spatial region

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality and Representations

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The semantic triangle revisited concepts termsobjects Representation and Reference First Order Reality about terms concepts

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminology Realist Ontology Representation and Reference First Order Reality about representational units universalsparticulars objects terms concepts

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminology Realist Ontology Representation and Reference First Order Reality about representational units universalsparticulars objects terms concepts

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminology Realist Ontology Representation and Reference First Order Reality about universalsparticulars objects terms concepts cognitive units communicative units representational units

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminology Realist Ontology Representation and Reference First Order Reality universalsparticulars cognitive units representational units (1) Entities with objective existence which are not about anything (2) Cognitive entities which are our beliefs about (1) communicative units (3)Representational units in various forms about (1), (2) or (3) Three levels of reality in Realist Ontology

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The three levels applied to diabetes management 1. First-order reality 2. Beliefs (knowledge) GenericSpecific DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MOLECULE PERSON DISEASE PATHOLOGICAL STRUCTURE PORTION OF INSULIN DRUG me my blood glucose level my NIDDM my doctor my doctor’s computer 3. Representation ‘person’‘drug’‘insulin’‘W. Ceusters’‘my sugar’

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminology is too reductionist What concepts do we need? How do we name concepts properly?

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The power of realism in ontology design Reality as benchmark ! 1. Is the scientific ‘state of the art’ consistent with biomedical reality ?

48. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The power of realism in ontology design Reality as benchmark ! 2. Is my doctor’s knowledge up to date?

49. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The power of realism in ontology design Reality as benchmark ! 3. Does my doctor have an accurate assessment of my health status?

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The power of realism in ontology design Reality as benchmark ! 4. Is our terminology rich enough to communicate about all three levels?

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The power of realism in ontology design Reality as benchmark ! 5. How can we use case studies better to advance the state of the art?

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Definitions: What is there to ‘define’?

53. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminological versus Ontological approach The terminologist defines: –‘a clinical drug is a pharmaceutical product given to (or taken by) a patient with a therapeutic or diagnostic intent’. (RxNorm) The ontologist thinks: –Does ‘given’ includes ‘prescribed’? –Is manufactured with the intent to … not sufficient? Are newly marketed products – available in the pharmacy, but not yet prescribed – not clinical drugs? Are products stolen from a pharmacy not clinical drugs? What about such products taken by persons that are not patients? –e.g. children mistaking tablets for candies.

54. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What does it mean ‘to define something’ ? ‘dog’ Dog-ness

55. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What does it mean ‘to define something’ ? ‘dog’ Dog-ness Under what circumstances am I allowed in my community to use the word ‘dog’ to denote some thing in reality ?

56. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What does it mean ‘to define something’ ? ‘dog’ Dog-ness What are the essential characteristics that distinguish dogs from other things ?

57. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What does it mean ‘to define something’ ? ‘dog’ Dog-ness How to determine whether the thing of which this picture is taken is a dog ?

58. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Examples for each type of definition 1.Appropriate usage of a word ‘girl’ – ‘chick’ 2.Essential characteristics distinguishing types from other types ‘death’ – ‘alive’ 3.Essential characteristics for something to be of a type death for a human: head separated from rest of the body 1 2 3

59. New York State Center of Excellence in Bioinformatics & Life Sciences R T U How can we know what dogs ‘really’ are ? ‘dog’ Dog-ness‘Dog-ness’ Human understanding and communication First-order reality generic specific

60. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Convention How can we know what dogs ‘really’ are ? ‘dog’ Dog-ness‘Dog-ness’ Human understanding and communication First-order reality generic specific Scientific discovery Case study Convention

61. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The right sort of science

62. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What is a good definition ? a good definition is a definition which gives an if-and- only-if condition to determine –The appropriate use of a term –The differentiation amongst types –Whether a particular is of a certain type One type of good definitions: Aristotelian definitions –An X is a Y which … A killing is an act which … –An act is a process which … »… ?????

63. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relations and the Structure of Reality

64. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relationships The way in which continuants and occurrents stand to each other. Example: –Prisoner:a person who is confined in prison The naïve solution: –Prisoner is-confined-in prison

65. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Problems with ‘Prisoner is-confined-in prison’ Structure of language does most often not reflect the structure of reality –‘is-confined-in’ is not an ontological relation What does it actually mean ? –Some/All prisoners are-confined-in some/all prisons ? Is a prisoner, while transferred from one prison to another one not anymore a prisoner ? Is the van or bus in which a prisoner is transported a prison ? …

66. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Three basic sorts of relationships universal particular humans are mammals Werner Ceusters instance-of human Werner Ceusters’ nose part-of Werner Ceusters

67. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Universals and classes universal PPPP PPPP PPPP instance-of extension-of member-of class Defined class e.g. human e.g. all humans e.g. all humans in this room

68. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Sorts of classes (1) Extension of a universal –e.g. all humans Defined class –a subset of the extension of a universal defined as being such that the members of this class exhibit an additional property which is (a) not shared by all instances of the universal, and (b) also (can be) exhibited by particulars which are not instances of that universal. –e.g. all human beings that suffer from pneumococcal pneumonia

69. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Sorts of classes (2) Compositional class (or ‘ad hoc class’) –an ad hoc collection of particulars such that some particulars are instances of a universal which is not instantiated by other particulars of that class –e.g. all sick human beings and polar bears

70. New York State Center of Excellence in Bioinformatics & Life Sciences R T U An exercise: what sorts of classes here ?

71. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology Terminology

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

73. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Primitive instance-level relationships 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

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

75. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Classes and time I-1 DC-10 at t class_member_of at t E: all human beings at t class_member_of at t HUMAN BEING instance_of at t extension_of at t subclass_of at t prisoners at t

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

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

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

79. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Occurrents, continuants and time

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

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

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

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

84. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Generalization of temporal parthood this walking walking Instance-of this leg moving leg moving process Is_a Instance-of part-of P part_of P1 = [definition] –for all p, –if Pp –then there is some p1 such that: P1p1 and p part_of p1

85. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Two sorts of temporal parthood (1) methis walking Has-participant at t 2 walking Instance-of my left leg part-of at t this leg moving leg moving part-of process Is_a Instance-of Has- Participant at t ‘longitudinal’: one process evolves as part of another one. May involve stronger relationships of other types, e.g. causal

86. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Two sorts of temporal parthood (2) Cuts cross temporal entities this walking this leg moving this foot moving t

87. New York State Center of Excellence in Bioinformatics & Life Sciences R T U On the practical importance of the particular / universal distinction

88. New York State Center of Excellence in Bioinformatics & Life Sciences R T U How many disorders are listed ? 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.

89. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘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 instance-of 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

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

91. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reasoning over instances and universals instance-of at t #105 caused by

92. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Essentials of Referent Tracking Theory 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.

93. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 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 Codes for types AND identifiers for instances

94. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The structure of reality and representations Level 1 Level 2 or 3 Level 3 Level 1, 2 or 3 unique identifiers

95. New York State Center of Excellence in Bioinformatics & Life Sciences R T U An example: ‘Adverse Events’ in ReMINE

96. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ReMINE’s notion of adverse event There is a double use of the term: 1.an ‘incident [that] occurred during the past and [is] documented in a database of adverse events’ –Stefano Arici, Paolo Bertele. ReMINE Deliverable D4.1 – RAPS Taxonomy: approach and definition. V1.0 (Final) August 8, 2008. (p21) … which is a ‘perdurant’ - ibidem (p26) … ‘that occurs to a patient’ - ibidem (p23) 2.an expectation of some future happening that can be prevented - ibidem (p23) But clearly, nothing which happened in the past (an occurrent), can be an expectation (a continuant) !

97. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Remind the 3-level distinction Level 1: –#1: an incident that happened in the past; Level 2: –#2: the interpretation by some cognitive agent that #1 is an adverse event; –#3: the expectation by some cognitive agent that similar incidents might happen in the future; Level 3: –#4: an entry in the adverse event database concerning #1; –#5: an entry in some other system about #3 for mitigation or prevention purposes.

98. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Assumptions about AE registration If an incident is judged to be an AE, –it should be registered in the database; –additional mitigation efforts should be conducted. If an incident is an AE, similar incidents may happen in the future. Judgments may be wrong.

99. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mismatches between reality and representations Some possibilities: 1.#1with unjustified absence of #2: #1 was not perceived at all, or not assessed as being an adverse event 2.Unjustified presence of #2: There was no #1 at all, or #1 was not an adverse event 3.Unjustified absence of #4 Same reasons as under (1) above Justified presence of #2 but not reported in the database –… Ceusters W, Smith B. A Realism-Based Approach to the Evolution of Biomedical Ontologies. Proceedings of AMIA 2006, Washington DC, 2006;:121-125.

100. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Multiple scenarios of co-existence Past incident relatedMitigation related AE happenedAE perceptionAE database entryInterpretedRegistered Case#1#2#4#3#5 1+---- 2++--- 3++-+- 4++-++ 5+++-- 6++++- 7+++++ 8----- 9-+--- 10-+-+- 11-+-++ 12-++-- 13-+++- 14-++++ Only cases 7 and 8 are faithful, justified presence and absence respectively

101. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Differing views on what counts as adverse event D1an observation of a change in the state of a subject assessed as being untoward by one or more interested parties within the context of a protocol-driven research or public health. BRIDG D2an event that results in unintended harm to the patient by an act of commission or omission rather than by the underlying disease or condition of the patient IOM D3any unfavorable and unintended sign (including an abnormal laboratory finding), symptom, or disease temporally associated with the use of a medical treatment or procedure that may or may not be considered related to the medical treatment or procedure NCI D4any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not necessarily have to have a causal relationship with this treatment CDISC D5an untoward, undesirable, and usually unanticipated event, such as death of a patient, an employee, or a visitor in a health care organization. Incidents such as patient falls or improper administration of medications are also considered adverse events even if there is no permanent effect on the patient. JTC D6an injury that was caused by medical management and that results in measurable disability. QUIC Ceusters W, Capolupo M, De Moor G, Devlies J. Introducing Realist Ontology for the Representation of Adverse Events. In: Eschenbach C, Gruninger M. (eds.) Formal Ontology in Information Systems, IOS Press, Amsterdam, 2008, 237-250.

102. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Need for change and belief management Distinct clinicians may hold different beliefs about whether a specific incident (e.g. #1) –really happened, –is of a specific sort, –counts as an adverse event depending on what definition they apply. They may differ in beliefs about –what caused the incident, –how to prevent future happenings of incidents of the same sort. They may change their beliefs over time.

103. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Keep track of all of this! The need … –To learn from judgment mistakes made in the past, –To identify differences in assessment skills, –To advance the state of the art by providing better evidence for identifying causes and consequences developing better treatments with less iatrogenic effects adopting better mitigation and prevention strategies. … requires the use of global and singular unique identifiers for each entity of interest in each of the three levels of reality.

104. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Keep in mind Whether an incident is an adverse event (under one or more definitions) –is a matter of objective fact, –is not a matter of consensus. What are matters of consensus, are: –definitions for what should be counted as adverse events but, –they can be applied wrongly, –they can be themselves in error; –policies about registration, –policies about mitigation and prevention, although, whether they are effective, is again a matter of objective fact.

105. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology development methodology (some examples from ReMINE)

106. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Design requirements for a good ontology Specify for each representational unit (RU): –whether it represents a universal or a particular, –whether it is at level 1, 2 or 3. Specify for each RU representing a universal of the type of its instances –e.g. occurrent, dependent or independent continuant, etc. Specify for each RU representing a particular whether the latter is: –the extension of the universal, –a defined class, –a compositional class, –a singular particular.

107. New York State Center of Excellence in Bioinformatics & Life Sciences R T U From terminology to ontology Analyze each entry of the ReMINE terminology: –Identify the universals of which instances must exist when the entry would be used faithfully for annotating a specific case, –Identify the relationships that must hold amongst these instances. Analyze the universals and relationships obtained in the previous step to build the core (domain) ontology: –Find for each hierarchy of universals the level at which further specialization does not influence the sort of relationships involved. –Build a representation of what is generic in reality in the context of AE management: Interrelate the universals and classes identified through individual term analysis. Map the ReMINE terminology to the core ontology by introducing defined and compositional classes (the application ontology). Design templates for annotation.

108. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example: ‘Absence_of_ventilation_Systems’ Required universals: –Ventilation system  Device  Independent continuant –Site  Independent continuant Required relationships: –located_in (at particular level *) –lacks with respect to located_in (**) Detailed annotation by means of RT-tuples: –If s were the site, e.g. the hospital or room in which there is no ventilation system, then: site 1 lacks Ventilation system with respect to contains at t 1 (or triple formulation: site 1 lacks_contains Ventilation system at t 1 ) * 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. ** Ceusters W, Elkin P, Smith B. Negative Findings in Electronic Health Records and Biomedical Ontologies: A Realist Approach. International Journal of Medical Informatics 2007;76:326-333

109. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example: ‘Presence_of_encumbrances_obstructions_in_corridors’ Universals: –Corridor  site –Object  independent continuant –Disposition to hinder passage  disposition –Motion  process Annotating the presence of obstructions in a corridor: –object 1 contained-in corridor 1 at t 1 –disposition-to-hinder-passage 1 inheres-in object 1 at t 1 Annotating when a hindering happened: –object 1 hinders motion 1 at t 2

110. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Insufficient_day_lighting’ For ‘insufficient day lighting’ to be a faithful annotation for a contributing factor to an AE, there must have been: –(1): a site (room, corridor, …), –(2): at least one period of time during which there was insufficient illumination of site (1), –(3): at least one period of time during it was day at site (1), –(4): at least one period of time during which (2) and (3) overlap.

111. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Time chart (combined with night illumination) t existence of site S 1 day (light) insufficient illumination insufficient day lighting night insufficient night lighting

112. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Insufficient illumination as a determinate quality Illumination is defined as a ‘determinable’ (*) BFO quality for which there is a ‘determinate’ value which can be described by the term ‘insufficient illumination’. The latter is a defined class because what counts as ‘insufficient’ is a matter of judgment. * Ingvar Johansson. Four Kinds of “Is_A” Relations: genus-subsumption, determinable-subsumption, specification, and specialization. In: Ingvar Johansson and Bertin Klein (Eds.): WSPI 2006: Contributions to the Third International Workshop on Philosophy and Informatics. Saarbrücken, May 3-4, 2006.

113. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Inherence of insufficient illumination Dependent entities inhere in independent entities –here: the illumination inheres in the site The insufficient quality of illumination does not need to be present all the time: –a time must be specified for the relationship –a temporal region (o-00026) exists during which the relationship holds

114. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Scattered temporal regions There exists also a temporal region during which it is day at site c-00024: o-00023 Finally, there is a temporal region (o-00027) which is the one that –overlaps with the region during which it is day at the site –overlaps with the region during which there is insufficient illumination

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

116. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Development methodology identify the terms in descriptions that denote portions of reality, determine the nature of these portions of reality, and more specifically: –the level of reality to which they belong, –whether they are specific or generic entities, further subdivide the entities into particulars (including classes), universals, or configurations as defined in any of the BFO-compatible ontologies identify the universals of which the particulars are instances, and the classes of which they are members, expand the representation, i.e. determine whether other portions of reality that are not explicitly denoted in the statements must be taken into account and if that is the case, apply the previous steps to them as well, identify the relations which are stated to hold between the particulars in line with the Relation Ontology and other relations used in BFO-compatible ontologies, assess whether particulars undergo relevant changes within the timeframe delineated by the protocol or guideline.

117. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Description of portion of reality The First Aid Doctor (FAD) evaluates vital parameters and possible alterations, acquires possible personal health documentation and asks for diagnostic services.

118. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Term identification First Aid Doctor FAD evaluates vital parameters alterations acquires personal health documentation asks diagnostic services The First Aid Doctor (FAD) evaluates vital parameters and possible alterations, acquires possible personal health documentation and asks for diagnostic services.

119. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Give specific and generic interpretations TermSpecific interpretationGeneric interpretation First Aid Doctor L1S-1a specific medical doctor providing first aid services in the Ospedale Niguarda Cà Granda G-2any medical doctor providing first aid services in the Ospedale Niguarda Cà Granda FADL1same as above evaluatesL1S-3a patient evaluation carried out by S-1 G-4the evaluation procedures carried out by a medical doctor in the Ospedale Niguarda Cà Granda when realization his role as first aid doctor vital parameters L1S-5a collection of the vital signs of that specific patient G-6the vital signs of a person

120. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Delineate relevant portions of reality TermSpecific interpretation First Aid Doctor L1S-1a specific medical doctor providing first aid services in the Ospedale Niguarda Cà Granda FADL1same as above evaluatesL1S-3a patient evaluation carried out by S-1 vital parameters L1S-5a collection of the vital signs of that specific patient alterationsL1S-7a collection out of S-5 of those vital parameters which are abnormal L2S-9collection of beliefs on the side of S-1 about which vital signs in S-5 are abnormal L3S-10collection of statements in some protocol or guideline issued by the Ospedale about what vital parameters are to be considered abnormal.

121. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relations between levels of reality

122. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Identification of single particulars, classes and universals TermParticularsUniversals This First Aid Doctor L1I-1that specific medical doctor providing first aid services in the Ospedale Niguarda Cà Granda human being L1I-11I-1’s medical doctor rolemedical doctor role L1DC-10the collection of medical doctors providing first aid services in the Ospedale Niguarda Cà Granda human being medical doctor role

123. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Strongly typed description language Variables: –CL, CL 1, … range over classes of any sort; –C, C 1,... range over continuant classes; –P, P 1,... range over process classes; –i, i 1, … range over particulars of any sort; –c, c 1,...range over continuant particulars; –p, p 1,...to range over process particulars; –t, t 1,...to range over instants or periods of time. Form: –v 1 relation v 2 t

124. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Describing configurations TermSpecificGenericRelations This First Aid Doctor L1I-1L1DC-10I-1 instance_of human being at t I-1 class_member_of DC-10at t L1I-11I-11 instance_of medical doctor role at t evaluatesL1I-2L3DC-11I-2 instance_of process DC-11 instance_of information artifact at t vital parameters L1CC-3L1CC-12CC-3 subclass_of CC-12at t alterationsL1CC-4L1CC-13CC-4 subclass_of CC-3at t CC-13 subclass_of CC-12at t L2DC-5L2DC-14DC-5 subclass_of DC-14at t L3DC-6L3DC-15DC-6 instance_of information artifact at t DC-15instance_of information artifact at t

125. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Expansion of the representation TermParticularsUniversalPlace in BFO This First Aid Doctor L1I-1that specific medical doctor providing first aid services in the Ospedale Niguarda Cà Granda human being independent continuant L1I-1001 the human being that undergoes I ‑ 2 human being independent continuant L1I-1002the role played by I-1001emergency patient role role L1I-1003the Ospedale Niguarda Cà Grandahealthcare facility object aggregate L1I-11I-1’s medical doctor rolemedical doctor role role First Aid Doctor L1DC-10the collection of medical doctors providing first aid services in I-1003 human being independent continuant

126. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Detailed representation R-1RelationR-2Comment I-1class_member_of at t1DC-10the specific first aid doctor is a member of the class of the first aid doctors of the Ospedale Niguarda Cà Granda I-11role_of at t2I-1he has the role of medical doctor I-1instance_of at t3human being he is a human being t1duringt2the time period during which he is an Ospedale first aid doctor is part of the time period during which he is a medical doctor t2duringt3the time period during which he is a medical doctor is part of the time period during which he is a human being

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

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

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

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

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

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

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

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

135. 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.

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

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

138. New York State Center of Excellence in Bioinformatics & Life Sciences R T U On mappings

139. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality-based mapping of care assessment instruments MDS Ontology U2U2 U3U3 U5U5 U4U4 U6U6 MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … U 11 U7U7 U 14 U 13 U 10 U 12 MDS terms U 17 U 16 U1U1 U9U9 U8U8 BFO Class-relations

140. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Adding the other assessments instruments U2U2 U1U1 U7U7 U 17 U9U9 U3U3 U5U5 U4U4 U6U6 U 11 U 10 U 14 U 12 U 13 U…U… OPO Ontology (MDS + CARE +…) MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … … MDS terms U 16 U8U8 BFO

141. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Adding the other assessments instruments U2U2 U1U1 U7U7 U 17 U9U9 U3U3 U5U5 U4U4 U6U6 U 11 U 10 U 14 U 12 U 13 U…U… OPO Ontology (MDS + CARE +…) MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … … … CARE 1 CARE 2 CARE 3 CARE 4 MDS terms CARE terms U 15 U 16 U8U8 BFO

142. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Poor man’s’ data linkage U2U2 U1U1 U7U7 U 17 U9U9 U3U3 U5U5 U4U4 U6U6 U 11 U 10 U 14 U 12 U 13 U…U… MDS Ontology MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … … MDS terms U 16 U8U8 pt4pt3 Patient data

143. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Data linkage using multiple instruments

144. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Problems with this level Exclusive focus on universals, ignoring that in data collection (almost) everything is about particulars. Therefore Referent Tracking must be brought in the picture.

145. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking solves this problem: It is true that: –(1) ‘All Americans have one mother’ –(2) ‘All Americans have one president’ But: –(1) ‘all Americans have a distinct mother’ –(2) ‘all Americans have a (numerically) identical president’

146. New York State Center of Excellence in Bioinformatics & Life Sciences R T U From ‘poor man’s’ to ‘rich man’s’ data linkage U2U2 U1U1 U7U7 U 17 U9U9 U3U3 U5U5 U4U4 U6U6 U 11 U 10 U 14 U 12 U 13 MDS Ontology MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … MDS terms U 16 U8U8 pt4pt3 Patient data formula

147. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Rich man’s data linkage: focus on particulars U6U6 U 11 MDS 3 MDS 4 pt4pt3 pt4 IUI-1 U6U6 IUI-2IUI-3 U 11 IUI-4IUI-5 pt3 Instance-of Particular relations

148. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Many more combinations possible The terms used in MDS 4 denote distinct particulars related to both patients One of the terms used in MDS 4 denotes the same particular for both patients

149. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Many more combinations possible If the same MDS (containing several referring terms) applies to different patients at t1, either –All terms denote always distinct particulars ‘patient is able to recall what he did yesterday’ –Some terms denote the same particular ‘patient is able to remember who he met yesterday’ If the same MDS applies to the same patient at distinct times: –Some terms may/may not denote the same particular ‘patient recognizes his room mate’ If the same term occurs in distinct MDS –May/may not denote the same particular (at any time)

150. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Applying realism-based ontologies for reasoning

151. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Setting up an institutional AE management system (1) Assign to all relevant particulars in the institution an Instance Unique Identifier (IUI): –Relevance is determined by the presence of a universal or defined class in the application ontology of which the particular is an instance or member –environment: e.g.: each corridor, each handrail, each room, each device –liveware: e.g.: each staff member, each patient, … –…

152. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Setting up an institutional AE management system (2) Use the IUIs in RT-tuples to describe for each particular: (1,2) –its relationships to other particulars, –its membership in defined classes, –its instantiation of universals. Update the annotations when there are changes in reality, the perception thereof or the ontology. (3) 1.Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78. 2.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. 3.Ceusters W, Smith B. A Realism-Based Approach to the Evolution of Biomedical Ontologies. Proceedings of AMIA 2006, Washington DC, 2006;:121-125.

153. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Consider connection to domotics and RFID systems Example: insufficient day/night illumination: –Light sensors and motion detectors in rooms and corridors and representations thereof in the AEMS –What are ‘sufficient’ illumination levels for specific sites is expressed in defined classes, –Each change in a detector is registered in real time in the AEMS, –Action-logic implemented in a rule-base system, f.i. to generate alerts.

154. New York State Center of Excellence in Bioinformatics & Life Sciences R T U RT-based representation (1): IUI assignment Reality level 1 #1: that corridor #3: that motion detector #4: that light detector #2: that lamp #6: that patient with RFID #7 #5: that RFID reader #8: that RFID reader #9: this elevator #10: 2nd floor of clinic B

155. New York State Center of Excellence in Bioinformatics & Life Sciences R T U RT-based representation (2): relationships (Semi-)stable relationships: –#1 instance-of ReM:Corridor since t1 –#2 instance-of ReM:Lamp since t2 –#2 contained-in #1 since t3 –#6 member-of ReM:Patient since t4 –#6 adjacent-to #7 since t4 –#18 instance-of ReM:Illumination since t1 –#18 inheres-in #1 since t1 –… Semi-stable because of: –lamps may be replaced –persons are not patients all the time –…  keeping track of these changes provides a history for each tracked entity

156. New York State Center of Excellence in Bioinformatics & Life Sciences R T U RT-based representation (3): rule base * Setting illumination requirements for lamp #2: –#18 member-of ReM:Insufficient illumination during t y if –t x part-of ReM:Daytime –# y1 instance-of ReM:Motion-detection –# y1 has-agent #3 at t y –t y part-oft x –# y2 instance-of ReM:Illumination measurement –# y2 has-agent #4 at t y –# y2 has-participant #18 at t y –# y2 has-result imr z at t y –imr z less-than 30 lumen else –t x part-of ReM:Night time –… endif * Exact format to be discussed with ReMINE partners

157. New York State Center of Excellence in Bioinformatics & Life Sciences R T U RT-based representation of events Imagine #6 (with RFID #7) walking through #1 –#2345 instance-of ReM:Motion-detection –#2345 has-agent #3 at t4 –#2346 instance-of ReM:RFID-detection –#2346 has-agent #5 at t4 –#2346 has-participant #7 at t4 –… Here, the happening of #2345 fires the rule explained on the previous slide. If imr z turns out to be too low, that might invoke another rule which sends an alert to the ward that lamp #2 might be broken. #2346 might trigger yet another rule, namely an alert for imminent danger for AE with respect to patient #6 …