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 MHI501 – Introduction to Health Informatics An Introduction to Formal Ontology in Bio-medicine SUNY at Buffalo - November 18, 2010 Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences Ontology Research Group 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 Overview Data and information Ontology (and relation to terminology) Ontological Realism: a specific methodology for doing ontological analyses Basic Formal Ontology (BFO): an upper level ontology based on Ontological Realism Biomedical Applications based on BFO: –the Open Biomedical Ontologies Foundry –the Ontology of General Medical Science –adverse event management in the ReMINE project Ontological Realism and (clinical) studies 2

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 Data and Information 3

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 A general belief: Better information Better care

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 ‘Information’ versus ‘informing’ Better information Better care Being better informed

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 A general belief:Being better informed Concerns primarily the delivery of information, independent of the quality of the information: Better information Better care Being better informed

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 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. pretty well covered long way to go

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 Today’s data generation and use observation & measurement data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome

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 Example 1: clinician Δ = outcome observation & measurement data organization diagnosis use add Generic beliefs verify treatment

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 Example 2: researcher observation & measurement data organization hypothesis use add Generic beliefs verify further R&D (instrument and study optimization) Δ = outcome

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 Example 3: device manufacturer / supplier observation & measurement data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome

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 12 Alarm signals: is biomedical science on the right track? Why most published research findings are false. Ioannidis JPA (2005). PLoS Med 2(8): e124. –Institute for Clinical Research and Health Policy Studies, Department of Medicine, Tufts-New England Medical Center, Tufts University School of Medicine, Boston, Massachusetts. Why Current Publication Practices May Distort Science. Young NS, Ioannidis JPA, Al-Ubaydli O (2008, October 7) PLoS Med 5(10): e201. doi:10.1371/journal.pmed.0050201. –Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland,

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 Some (well-documented) claims A research finding is less likely to be true when –the studies conducted in a field are smaller; –effect sizes are smaller; –there is a greater number and lesser preselection of tested relationships; –there is greater flexibility in designs, definitions, outcomes, and analytical modes; –there is greater financial and other interest and prejudice; –more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. For many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. Ioannidis JPA (2005) Why most published research findings are false. PLoS Med 2(8): e124.

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 Quality Information must cover … EHR-EMR-ENR-… PHR Various modality related databases –Lab, imaging, … Textbooks Classification systems Terminologies Ontologies Patient-specific information Scientific “knowledge” 1 2 3

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 Means to structure the available information Key question: on what should the structure be based ?

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 What is the structure based on ? (1) Classification systems: on ‘properties’ of patients which are of importance for the purposes the system has been designed http://www.who.int/classifications/en/

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 What is the structure based on ? (2) Terminologies: –on ‘concepts’ But terminologists fail to give a good answer on what a concept is

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 What is the structure based on ? (3) ‘Ontologies’ (mainstream view): –on ‘concepts’ when designed by terminologists –on ‘classes’ when designed by software engineers and computer scientists –a class is a construct that is used as a blueprint to create objects of that class. ?blueprintobjects –a class is a cohesive package that consists of a particular kind of metadata. ??cohesivemetadata –a class usually represents a noun, such as a person ??? http://en.wikipedia.org/wiki/Class_(computer_science)

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 What should the structure be based on ? on the structure of Reality !!! (yes, I’m shouting)

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 Ontology 20

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 ‘Ontology’ In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; In computer science and many biomedical informatics applications: –An ontology (plural: ontologies) is a shared and agreed upon conceptualization of a domain; The realist view within the Ontology Research Group combines the two: –We use Ontological Realism, a specific methodology that uses ontology as the basis for building high quality ontologies, using reality as benchmark.

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 Three major views on reality Basic questions: –What does a general term such as ‘diabetes’ refer to? –Do generic things exist? yes: in particulars perhaps: in minds no UniversalConceptCollection of particulars RealismConceptualismNominalism

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 Dominant view in computer science is conceptualism Basic questions: –What does a general term such as ‘tree’ refer to? –Do generic things exist? yes: in particulars perhaps: in minds no UniversalConceptCollection of particulars RealismConceptualismNominalism

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 Dominant view in computer science is conceptualism RealismConceptualismNominalism Semantic Triangle concept objectterm Embedded in Terminology

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 25 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.

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 Why is this important ? Not as much for humans: –Our ‘minds’ are very good in resolving ambiguities, or fill in gaps, even at ‘unconscious’ levels. But for machines (computers, software): –They can’t deal with imprecise, vague or ambiguous statements.

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 The semantic triangle revisited concepts termsobjects Representation and Reference First Order Reality about terms concepts

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 Terminology Realist Ontology Representation and Reference First Order Reality about representational units universalsparticulars objects terms concepts

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 Terminology Realist Ontology Representation and Reference First Order Reality about representational units universalsparticulars objects terms concepts

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 Terminology Realist Ontology Representation and Reference First Order Reality about universalsparticulars objects terms concepts cognitive units communicative units representational units

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 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

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 Ontological Realism 32

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 No serious scholar should work with ‘concepts’

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 Slow penetration of the idea …

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 More serious scholars become convinced … what is a concept description a description of?

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 but Kantians will never …

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

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

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 1.There is an external reality which is ‘objectively’ the way it is; 2.That reality is accessible to us; 3.We build in our brains cognitive representations of reality; 4.We communicate with others about what is there, and what we believe there is there. The basis of Ontological Realism 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

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 Ontological Realism makes three crucial distinctions 1.Between data and what data are about; 2.Between continuants and occurrents; 3.Between what is generic and what is specific. 40 Smith B, Ceusters W. Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies. Applied Ontology, 2010. (forthcoming)

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 Ontological Realism makes crucial distinctions Between data and what data are about: –Level 1 entities (L1): everything what exists or existed some are referents (‘are’ used informally) some are L2, some are L3, none are L2 and L3 –Level 2 entities (L2): beliefs all are L1 some are about other L1-entities but none about themselves –Level 3 entities (L3): expressions all are L1, none are L2 some are about other L1-entities and some about themselves 41

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

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 L1 L2 L3 43

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 OBO Foundry L1 L2 L3 44

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 Ontological Realism makes crucial distinctions Between data and what data are about; Between continuants and occurrents: –obvious differences: a person versus his life a disease versus its course space versus time –more subtle differences (inexistent for flawed models e.g. HL7-RIM) : observation (data-element) versus observing diagnosis versus making a diagnosis message versus transmitting a message 45

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 Between ‘generic’ and ‘specific’ L1. First-order reality L2. 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 L3. Representation ‘person’‘drug’‘insulin’‘W. Ceusters’‘my sugar’ Referent TrackingBasic Formal Ontology GenericSpecific 46

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 Observations and similarities

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 Observations and similarities Are these pictures of concepts or of horses ? Is this a sensible question: ‘What concepts have tails and do …?’

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 Observations and similarities Are these pictures of concepts? Are these pictures of anything at all? If concepts are in brains, that must be awfully big brains!

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 Basic Formal Ontology: an upper ontology based on Ontological Realism 50

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 A useful parallel: Alberti’s grid reality representation Ontological theory

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 Basic components of the BFO view on 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

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 53 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

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 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

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 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

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 Particulars versus Universals some particular some universal instanceOf … entities on either site cannot ‘cross’ this boundary every particular is an instance of at least one universal for every universal there is or has been at least one instance

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 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

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 instanceOf at t 2 instanceOf at t 1 instanceOf at t 2 The importance of temporal indexing this-1’s stomach benign tumor instanceOf at t 1 this-4 malignant tumor partOf at t 1 stomach partOf at t 2

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 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

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 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

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 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

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 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

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 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

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 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

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 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

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 Dependent continuants Realized –Quality:redness (of blood) Realizable –Function:to flex (of knee joint) –Role:student –Power:boss –Disposition:brittleness (of a bone) 3

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 67 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

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 Disposition A disposition is a realizable entity which is such that (1) if it ceases to exist, then its bearer is physically changed, (2) whose realization occurs, in virtue of the bearer’s physical make-up, when this bearer is in some special physical circumstances

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 Unconstrained reasoning OWL-DL reasoning Sorts of relations U1U2 P1 P2 UtoU: isa, partOf, … PtoU: instanceOf, lacks, denotes… PtoP: partOf, denotes, subclassOf,…

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 Relation Ontology Continuant Occurrent process, event Independent Continuant ~ thing Dependent Continuant................ universals particulars has_participant inheres_in instance_of (at t) isa

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 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.

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 Is_a is defined over instance-of (2) human being living creature me universals particulars is_a instance-of at t therefore

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 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

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 Transformation Derivation continuation fusion fission

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 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

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 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

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 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 ?

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 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 ?

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 tt t instanceOf The essential pieces material object spacetime region me some temporal region my life my 4D STR some spatial region history spatial region temporal region dependent continuant some quality located-in at t … at t participantOf at toccupies projectsOn projectsOn at t

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 Biomedical Applications of Ontological Realism 80

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 The OBO Foundry a family of interoperable biomedical reference ontologies built around the Gene Ontology (GO) at its core and using the same principles as the GO a modular annotation catalogue of English phrases each module created by experts from the corresponding scientific community http://obofoundry.org

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

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 OBO Website

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 RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) OBO Foundry ontologies in BFO-dress

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 Ontology of General Medical Science First ontology in which the L1/L2/L3 distinction is used Scheuermann R, Ceusters W, Smith B. Toward an Ontological Treatment of Disease and Diagnosis. 2009 AMIA Summit on Translational Bioinformatics, San Francisco, California, March 15-17, 2009;: 116-120. Omnipress ISBN:0-9647743-7-22009 AMIA Summit on Translational Bioinformatics

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 To be a consistent, logical and extensible framework (ontology) for the representation of –features of disease –clinical processes –results Goal of OGMS

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 Motivation Clarity about: –disease etiology and progression –disease and the diagnostic process –phenotype and signs/symptoms

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 Big Picture

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 a disease is a disposition rooted in a physical disorder in the organism and realized in pathological processes. etiological process produces disorder bears disposition realized_in pathological process produces abnormal bodily features recognized_as signs & symptomsinterpretive process produces diagnosis participates_in Approach

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 Etiological process - phenobarbitol- induced hepatic cell death –produces Disorder - necrotic liver –bears Disposition (disease) - cirrhosis –realized_in Pathological process - abnormal tissue repair with cell proliferation and fibrosis that exceed a certain threshold; hypoxia-induced cell death –produces Abnormal bodily features –recognized_as Symptoms - fatigue, anorexia Signs - jaundice, splenomegaly Symptoms & Signs –used_in Interpretive process –produces Hypothesis - rule out cirrhosis –suggests Laboratory tests –produces Test results – documentation of elevated liver enzymes in serum –used_in Interpretive process –produces Result - diagnosis that patient X has a disorder that bears the disease cirrhosis Cirrhosis - environmental exposure

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 Influenza - infectious Etiological process - infection of airway epithelial cells with influenza virus –produces Disorder - viable cells with influenza virus –bears Disposition (disease) - flu –realized_in Pathological process - acute inflammation –produces Abnormal bodily features –recognized_as Symptoms - weakness, dizziness Signs - fever Symptoms & Signs –used_in Interpretive process –produces Hypothesis - rule out influenza –suggests Laboratory tests –produces Test results – documentation of elevated serum antibody titers –used_in Interpretive process –produces Result - diagnosis that patient X has a disorder that bears the disease flu But the disorder also induces normal physiological processes (immune response) that can result in the elimination of the disorder (transient disease course).

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 Disorder =def. – A causally linked combination of physical components that is –(a) clinically abnormal and –(b) maximal, in the sense that it is not a part of some larger such combination. Pathological Process =def. – A bodily process that is a manifestation of a disorder and is clinically abnormal. Foundational Terms (1)

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 Clinically abnormal - something is clinically abnormal if: –(1) is not part of the life plan for an organism of the relevant type (unlike aging or pregnancy), –(2) is causally linked to an elevated risk either of pain or other feelings of illness, or of death or dysfunction, and –(3) is such that the elevated risk exceeds a certain threshold level.

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 Disorder =def. – A causally linked combination of physical components that is (a) clinically abnormal and (b) maximal, in the sense that it is not a part of some larger such combination. Pathological Process =def. – A bodily process that is a manifestation of a disorder and is clinically abnormal. Disease =def. – A disposition (i) to undergo pathological processes that (ii) exists in an organism because of one or more disorders in that organism. Foundational Terms (2)

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 Clinical Picture =def. – A representation of a clinical phenotype that is inferred from the combination of laboratory, image and clinical findings about a given patient. Diagnosis =def. – A conclusion of an interpretive process that has as input a clinical picture of a given patient and as output an assertion to the effect that the patient has a disease of such and such a type. Diagnosis

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 Obvious? ‘Diseases and diagnoses are the principal ways in which illnesses are classified and quantified, and are vital in determining how clinicians organize health care.’ Ann Fam Med 1(1):44-51, 2003. ‘MedDRA […] is a standardized dictionary of medical terminology [ … which …] includes terminology for symptoms, signs, diseases and diagnoses.’ Medical Dictionary for Regulatory Activities

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 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 disease is believed to be an instance. #56 John’s Pneumonia #78 John’s relevant portion of pneumococs Pneumococcal pneumonia caused by Instance-of at t1 Disease isa

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 Some motivations and consequences (1) No use of debatable or ambiguous notions such as proposition, statement, assertion, fact,... 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

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 Some motivations and consequences (2) A diagnosis can be of level 2 or level 3, i.e. either in the mind of a cognitive agent, or in some physical form. Allows for a clean interpretation of assertions of the sort ‘these patients have the same diagnosis’:  The configuration of representational units is such that the parts which do not refer to the particulars related to the respective patients, refer to the same portion of reality.

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 Distinct but similar diagnoses #56 John’s Pneumonia #78 John’s portion of pneumococs Pneumococcal pneumonia caused by #956 Bob’s pneumonia #2087 Bob’s portion of pneumococs caused by Instance-of at t1Instance-of at t2

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 Some motivations and consequences (3) Allows evenly clean interpretations for the wealth of ‘modified’ diagnoses: –With respect to the author of the representation: ‘nursing diagnosis’, ‘referral diagnosis’ –When created: ‘post-operative diagnosis’, ‘admitting diagnosis’, ‘final diagnosis’ –Degree of belief: ‘uncertain diagnosis’, ‘preliminary diagnosis’

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 Adverse events 102

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 ReMINE Project Ceusters W, Capolupo M, Smith B, De Moor G. An Evolutionary Approach to the Representation of Adverse Events. In: Medical Informatics Europe 2009, Sarajevo, Bosnia and Herzegovina, August 31, 2009. Studies in health technology and informatics 150;:537-541.

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 Ontology development in ReMINE ReMINE Taxonomies ReMINE Adverse Event Domain Ontology ReMINE Application Ontology ReMINE Application Ontology ReMINE Application Ontology ReMINE Application Ontologies ReMINE Taxonomies ReMINE Taxonomies ReMINE Taxonomies Description of specific adverse event domains (childbirth, patient transfer,..) as cognized by human beings Realism-based, purpose independent representation of the portion of reality described in the taxonomies Purpose dependent reformulations of the parts of RAEDO which are relevant for a specific domain support annotation support reasoning higher order logic description logic

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 Risk Manager’s Event Administration System ReMINE Taxonomy Annotated Events

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 ReMINE’s notion of adverse event 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)

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 Terminologists agree, ontologists think … Can something which is an incident be at the same time an expectation ? Can something which is an incident a time t, later become an adverse event simply because it [?] has been entered in a database ? Can adverse events really occur in software ? …

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 108 Intermediate conclusion The ReMINE taxonomy (and all concept-based terminologies and ‘ontologies’ in general) provides a distorted view of reality. For good reasons: the distortion is such that –it reflects a pragmatic view on what is relevant for the purposes it is designed, –it does away with complexities that do not help human beings in doing a better job. But with some negative consequences: –reusability out of the ReMINE context is hampered, –integration with other descriptive systems becomes cumbersome, and –advanced reasoning turns out to be impossible.

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 Using the 3 levels and the particular/universal/class distinctions 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.

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 Allows appropriate error management 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.

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 Part of the ReMINE Domain Ontology

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 Higher order logical representation an incident (#1) that happened at time t2 to a patient (#2) after some intervention (#3 at t1) is judged at t3 to be an adverse event, thereby giving rise to a belief (#4) about #1 on the part of some person (#5, a caregiver as of time t6). This requires the introduction (at t4) of an entry (#6) in the adverse event database (#7, installed at t0).

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 Advantages Synchronisation of two distinct representations of the same reality: –taxonomies: user-oriented view data annotation Domain ontology compatible with OBO-Foundry ontologies: –no overlap, –easier to re-use. Not only tracking of incidents, but also: –how well individual clinicians and organizations manage adverse events, –how well one learns from past experiences. –ontologies: realism-based view unconstrained reasoning

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 Use in study design and data collection

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 Typical approach (1) Building a huge matrix with patient cases in one dimension and patient characteristics in the other dimension Cases Characteristics ch1ch2ch3ch4ch5ch6... case1 case2 case3 case4 case5 case6...

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 Typical approach (2) Use statistical correlation techniques to find associations between characteristics and (dis)similarities between cases Cases Characteristics ch1ch2ch3ch4ch5ch6... case1 case2 case3 case4 case5 case6...

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 Fundamental questions 1.What is a characteristic ? 2.What (sorts of) characteristics (relevant for, e.g., psychiatry) go in here ? 3.How can we make distinct studies comparable? 4.Because such matrices tend to become huge, how can we make analysis feasible ? 5.How can we make results re-usable?

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 Q1: what is a characteristic ? –it is for sure not a category entities can belong to: there is no natural class of entity for which the name ‘characteristic’ would be appropriate; –there is also no particular entity that you could point to and state ‘that over there is the only existing characteristic’ –thus: there are no characteristics, there is just the term ‘characteristic’ which is used to describe that some entities are (acknowledged to be) in some way of interest in some context and for some purpose.

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 This requires rephrasing Q2 What (sorts of) characteristics (relevant for psychiatry) go in here? What entities described as being characteristic for psychiatric purposes should be represented here?

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 Examples Continuant Occurrent Independent Continuant Dependent Continuant UniversalsParticulars portion of C 17 H 19 ClN 2 S. HCl human being gene portion of chlorpromazine in this tablet me the HTR2A gene on chromosome 13 of the most frontal cell in the tip of my nose shape temperature length the shape of my nose the temperature of the chlorpromazine tablet in front of me the length of that HTR21 gene change in shape motion rise in temperature unfolding of a DNA molecule the circulation of a chlorpromazine molecule in my bloodstream the rise of my body temperature while teaching this seminar

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 Q3: How can we make distinct studies comparable? Map any characteristic used to relevant, standard and high quality ontologies

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 The positive effects of appropriate mappings

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 The positive effects of appropriate mappings identification of ontological relations prior to statistical correlation: –ch1 and ch4 –ch1 and ch5 –ch1 and ch2 –… Contributes to answering ‘Q4: how can we make analysis feasible’ –this method allows for data- reduction without information loss.

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 Filling the grid We know now that labels from appropriate ontologies go here But, what goes here?

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 Remember we had this … Continuant Occurrent Independent Continuant Dependent Continuant UniversalsParticulars portion of C 17 H 19 ClN 2 S. HCl human being gene portion of chlorpromazine in this tablet me the HTR2A gene on chromosome 13 of the most frontal cell in the tip of my nose shape temperature length the shape of my nose the temperature of the chlorpromazine tablet in front of me the length of that HTR21 gene change in shape motion rise in temperature unfolding of a DNA molecule the circulation of a chlorpromazine molecule in my bloodstream the rise of my body temperature while talking here

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 Or after transposition … Continuant Occurrent Independent Continuant Dependent Continuant Universals Particulars portion of C 17 H 19 ClN 2 S. HCl portion of chlorpromazine in the tablet in front of me me the HTR2A gene on chromosome 13 of the most frontal cell in the tip of my nose the shape of my nose the temperature of the chlorpromazine tablet in front of me the length of that HTR21 gene unfolding of a DNA molecule the circulation of a chlorpromazine molecule in my bloodstream the rise of my body temperature while teaching this seminar human being geneshapetemperaturelengthchange in shape motionrise in temperature

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 … and for many patients Particulars case1 case2 case3 case4 case5 case6 case7 case8 …..................................

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 Referent Tracking unique identification by means of ‘codes’ unique identification by means of ‘instance unique identifiers’