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 Institute for Healthcare Informatics IADR 2013 Satellite Workshop Workshop on Biomedical Ontology and Referent Tracking: Introduction to Basic Principles March 20, 2013 – Washington State Convention Center, Seattle, WA Werner CEUSTERS, MD Center of Excellence in Bioinformatics and Life Sciences, Ontology Research Group, Institute for Healthcare Informatics, Department of Psychiatry University at Buffalo, NY, USA http://www.org.buffalo.edu/RTU Richard OHRBACH, DDS, PhD Department of Oral Diagnostic Sciences University at Buffalo, NY, USA http://dental.buffalo.edu/asp/home.asp?id=579

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 Institute for Healthcare Informatics Tutorial overview The problems of terminology-based ontologies, Ontological Realism: –Principles, –Basic Formal Ontology (BFO), –Ontology of General Medical Science (OGMS), –Referent Tracking (RT), Building an ontology for integrating clinical datasets about orofacial pain. 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 Institute for Healthcare Informatics Why this tutorial, this way ? 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 Institute for Healthcare Informatics To avoid nonsense like this (SNOMED CT 2011©) 4

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 Institute for Healthcare Informatics To avoid nonsense like this (SNOMED CT 2011©) 5 The problem: very bad terminological design

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 Institute for Healthcare Informatics And like this: 6 http://www.idi.ntnu.no/emner/tdt4210/2004/2004link/ovinger/smerteontologi_oving/ The problem: very bad ontological design: −erroneous domain analysis −violations against representation language semantics

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 Institute for Healthcare Informatics 7 I hope this is a joke http://www.mkbergman.com/http://www.mkbergman.com/ Last accessed: Jan 31, 2012 reproduction licensed through: http://creativecommons.org/licenses/by-nc-sa/2.5/

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 Institute for Healthcare Informatics Part 1 The problems of terminology-based ontologies 8

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 Institute for Healthcare Informatics Language is ambiguous ‘I know that you believe that you understood what you think I said, but I am not sure you realize that what you heard is not what I meant.’ –Robert McCloskey, State Department spokesman (attributed). http://www.quotationspage.com/quotes/Robert_McCloskey/

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 Institute for Healthcare Informatics Language is ambiguous Often we can figure it out … in Miami hotel lobby warning on plastic bag in Miami bar

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 Institute for Healthcare Informatics Language is ambiguous in Amsterdam hotel elevator Sometimes, we can not …

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 Institute for Healthcare Informatics It is worse for machines... “John Doe has a pyogenic granuloma of the left thumb” We see:     The machine sees:

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 Institute for Healthcare Informatics It is worse for machines... John Doe pyogenic granuloma of the left thumb The XML misunderstanding We see:    The machine sees:

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 Institute for Healthcare Informatics The clever (?) business man and his XML card John Nitwit 524 Moon base avenue Utopia … Is this the name of the business card or of the business card owner?

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 Institute for Healthcare Informatics Intermediate conclusion We need for sure methods and techniques that allow: –people to express exactly what they mean, –people to understand exactly what is communicated to them, –machines to communicate information without any distortion. If information overload is a problem, we also need methods and techniques that allow machines to understand exactly what is communicated to them.

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 Institute for Healthcare Informatics Unfortunately … Traditional terminology alone is not going to do the job, Not even when you express it (naively) in OWL !!! (yes, I am shouting) 16

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 Institute for Healthcare Informatics But what the word ‘concept’ denotes, is usually not clarified and users of it often refer to different entities in a haphazard way: Why not: most terminologies are ‘concept’-based Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

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 Institute for Healthcare Informatics SNOMED about diseases and concepts (until 2010) ‘Disorders are concepts in which there is an explicit or implicit pathological process causing a state of disease which tends to exist for a significant length of time under ordinary circumstances.’ And also: “Concepts are unique units of thought”. Thus: Disorders are unique units of thoughts in which there is a pathological process …??? And thus: to eradicate all diseases in the world at once we simply should stop thinking ?

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 Institute for Healthcare Informatics But what the word ‘concept’ denotes, is usually not clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort Most terminologies are ‘concept’-based Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

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 Institute for Healthcare Informatics But what the word ‘concept’ denotes, is usually not clarified and users of it often refer to different entities in a haphazard way, the result being: chaos Most terminologies are ‘concept’-based Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

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 Institute for Healthcare Informatics Some examples 21

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 Institute for Healthcare Informatics 22 Border’s classification of medicine: what’s wrong ? Medicine –Mental health –Internal medicine Endocrinology –Oversized endocrinology Gastro-enterology... –Pediatrics –... –Oversized medicine Refer to the size of the books that do not fit on a normal Border’s Bookshop shelf

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 Institute for Healthcare Informatics Similar mistake in ICHD ‘13.1.2.4 Painful trigeminal neuropathy attributed to MS plaque’ ‘attributed to’ relates to somebody’s opinion about what is the case, not to what is the case. –the mistake: a feature on the side of the clinician – his (not) knowing - is taken to be a feature on the side of the patient. Similar mistakes: –‘Probable migraine’ –‘facial pain of unknown origin’ (not in ICHD). 23

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 Institute for Healthcare Informatics 24 Is this a good idea ? Cover subject matter of papers Cover the form of papers

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 Institute for Healthcare Informatics 25 Principle A representation should not mix object language and meta language –object language describes the referents in the subject domain –meta language describes the object language

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 Institute for Healthcare Informatics 26 Geographic Locations: a good hierarchy ? Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + mereological mess mixture of geographic entities with socio- political entities mixture of space and time

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 Institute for Healthcare Informatics 27 Geographic Locations [Z01] Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + Ancient Lands [Z01.586.035] + Austria-Hungary [Z01.586.117] Commonwealth of Independent States [Z01.586.200] + Czechoslovakia [Z01.586.250] + European Union [Z01.586.300] Germany [Z01.586.315] + Korea [Z01.586.407] Middle East [Z01.586.500] + New Guinea [Z01.586.650] Ottoman Empire [Z01.586.687] Prussia [Z01.586.725] Russia (Pre-1917) [Z01.586.800] USSR [Z01.586.950] + Yugoslavia [Z01.586.980] +

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 Institute for Healthcare Informatics 28 Geographic Locations [Z01] Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + Ancient Lands [Z01.586.035] + Austria-Hungary [Z01.586.117] Commonwealth of Independent States [Z01.586.200] + Czechoslovakia [Z01.586.250] + European Union [Z01.586.300] Germany [Z01.586.315] + Korea [Z01.586.407] Middle East [Z01.586.500] + New Guinea [Z01.586.650] Ottoman Empire [Z01.586.687] Prussia [Z01.586.725] Russia (Pre-1917) [Z01.586.800] USSR [Z01.586.950] + Yugoslavia [Z01.586.980] +

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 Institute for Healthcare Informatics 29 Principle A hierarchical structure should not represent distinct hierarchical relations unless they are formally characterized

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 Institute for Healthcare Informatics 30 Diabetes Mellitus in MeSH 2008 ? Different set of more specific terms when different path from the top is taken. 2

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 Institute for Healthcare Informatics 31 MeSH: some paths from top to Wolfram Syndrome Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Diseases Optic Atrophy Optic Atrophies, Hereditary Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus Female Urogenital Diseases and Pregnancy Complications Female Urogenital Diseases

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 Institute for Healthcare Informatics 32 What would it mean if used in the context of a patient ? Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Diseases Optic Atrophy Optic Atrophies, Hereditary has Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Female Urogenital Diseases and Pregnancy Complications Female Urogenital Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus ??? … has

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 Institute for Healthcare Informatics 33 Principle If a particular (individual) is related in a specific way to a ‘class’, it should also be related in the same way to all the ‘superclasses’ of that class –Technically: “… to all the classes that subsume that class”

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 Institute for Healthcare Informatics 34 Body Regions [A01] –Extremities [A01.378] Lower Extremity [A01.378.610] –Buttocks [A01.378.610.100] –Foot [A01.378.610.250] »Ankle [A01.378.610.250.149] »Forefoot, Human [A01.378.610.250.300] + »Heel [A01.378.610.250.510] –Hip [A01.378.610.400] –Knee [A01.378.610.450] –Leg [A01.378.610.500] –Thigh [A01.378.610.750] What’s wrong ? MeSH Tree Structures – 2007

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 Institute for Healthcare Informatics 35 SNOMED-CT: what is wrong here? nose bones fracture false synonymy

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 Institute for Healthcare Informatics 36 Coding / Classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

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 Institute for Healthcare Informatics 37 A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = = Coding / Classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

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 Institute for Healthcare Informatics Equivalent mistake in ICHD 13.1. Trigeminal Neuralgia –13.1.2 Painful Trigeminal Neuropathy ICHD definitions: 1.‘neuralgia’ = pain in the distribution of nerve(s) 2.‘pain’ = a sensorial and emotional experience... 3.‘neuropathy’ = a disturbance of function or pathological change in a nerve. Several mismatches: –(1) and (2): neuralgia is a sensorial and emotional experience in the distribution of nerve(s) ? –(1) and (3): with much of goodwill, one could accept neuropathy to subsume neuralgia, but chapter 13 claims the opposite for the trigeminal case. 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 Institute for Healthcare Informatics Summary of Part 1 39

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 Institute for Healthcare Informatics 40 Summary of current deficiencies in traditional and formal terminologies (1) Terms often require “reading in context”, agrammatical constructions (paper-based indexing), semantic drift as one moves between hierarchies, not (yet) useful for natural language understanding by software (but were not designed for that purpose),

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 Institute for Healthcare Informatics 41 Summary of current deficiencies in traditional and formal terminologies (2) labels for terms do not correspond with formal meaning, underspecification (leading to erroneous classification in DL-based systems), overspecification (leading to wrong assumptions with respect to instances).

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 Institute for Healthcare Informatics 42 Take-home message Concept-based terminology (and standardisation thereof) is there as a mechanism to improve understanding of messages by humans. It is NOT the right device –to explain why reality is what it is, how it is organised, etc., (although it is needed to allow communication), –to reason about reality, –to make machines understand what is real, –to integrate across different views, languages, conceptualisations,...

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 Institute for Healthcare Informatics Principles of Ontological Realism 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 Institute for Healthcare Informatics ‘Ontology’ In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other;

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 Institute for Healthcare Informatics Philosophy and ‘philosophy’ The Ontology of the Pain Antoine Arab 45 http://www.scribd.com/doc/22719509/The-Ontology-of-the-Pain

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 Institute for Healthcare Informatics Philosophy and ‘philosophy’ The Ontology of the Pain Antoine Arab 46 http://www.scribd.com/doc/22719509/The-Ontology-of-the-Pain

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 Institute for Healthcare Informatics ‘Ontology’ In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; –by some philosophers taken to be synonymous with ‘metaphysics’ while others draw distinctions in many distinct ways (the distinctions being irrelevant for this talk), but almost agreeing on the following classification: metaphysics  studies ‘how is the world?’ –general metaphysics  studies general principles and ‘laws’ about the world »ontology  studies what type of entities exist in the world –special metaphysics  focuses on specific principles and entities –distinct from ‘epistemology’ which is the study of how we can come to know about what exists. –distinct from ‘terminology’ which is the study of what terms mean and how to name things.

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 Institute for Healthcare Informatics A legitimate metaphysical question: does pain exist? 48 http://evans-experientialism.freewebspace.com/ed_pain.htm

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 Institute for Healthcare Informatics Metaphysics, ontology and pain A metaphysical account of pain: –determining the nature of pain, identifying what all pains have in common in virtue of which they are pains. An ontological account of pain: –determining the ontological commitments acquired by countenancing pains in one's theory. e.g.: whether one's theory of pain entails the existence of anything immaterial is not a metaphysical question but rather an ontological one. Guillermo Hurtado and Oscar Nudler (eds.), The Furniture of the World: Essays in Ontology and Metaphysics, Rodopi, 2012, 336pp., $94.50 (pbk), ISBN 9789042035034.

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 Institute for Healthcare Informatics Metaphysics, ontology and pain These two questions are independent: –a functionalist (#1) about pain may reject any ontological commitment to immaterial things, aligned with physicalism rather than dualism on the ontological question. –#1 may disagree with both metaphysical physicalism and dualism about the nature of pain. to account for what all pains have in common qua pains: –the functionalist would invoke a certain common functional role –the physicalist would invoke something physical –the dualist something immaterial. Guillermo Hurtado and Oscar Nudler (eds.), The Furniture of the World: Essays in Ontology and Metaphysics, Rodopi, 2012, 336pp., $94.50 (pbk), ISBN 9789042035034.

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 Institute for Healthcare Informatics Distinct questions. What type are they of? Terminological: –what does ‘pain’ mean ? Metaphysical: –what have all pains in common in virtue of which they are pains? Ontological: –what type of entity is pain? Onto-terminological: –what, if anything at all, does ‘pain’ denote? Epistemological: –how can we find out whether something is pain? 51

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Unfortunately, ‘ontology’ denotes ambiguously 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;

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 Institute for Healthcare Informatics Semantic Applications use Computer science approach to ontology 53 Ontology Authoring Tools Reasoners create Domain Ontologies

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 Institute for Healthcare Informatics Semantic Applications use Computer science approach to ontology 54 Ontology Authoring Tools Reasoners create Domain Ontologies the logic in reasoners: guarantees consistent reasoning, does not guarantee the faithfulness of the representation.

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 Institute for Healthcare Informatics 55 Consistent reasoning with nonsensical representations Ceusters W, Smith B, Flanagan J. Ontology and Medical Terminology: why Descriptions Logics are not enough. Proceedings of the conference Towards an Electronic Patient Record (TEPR 2003), San Antonio, 10-14 May 2003 (electronic publication 5pp)

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 Institute for Healthcare Informatics Philosophical approach to ontology 56 Ontological Realism: uses ontology as philosophical discipline to build ontologies as faithful representations of reality.

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 Institute for Healthcare Informatics

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 Institute for Healthcare Informatics 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

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 Institute for Healthcare Informatics 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. 59 Smith B, Ceusters W. Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies. Applied Ontology, 2010.

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 Institute for Healthcare Informatics 60

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 Institute for Healthcare Informatics L1 - L2 L3 61 Linguistic representations about (1), (2) or (3) Clinicians’ beliefs about (1) Entities (particular or generic) with objective existence which are not about anything Representations First Order Reality

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 Institute for Healthcare Informatics observation & measurement A crucial distinction: data and what they are about data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome First- Order Reality Representation is about 62

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 Institute for Healthcare Informatics Mixing L1- and L3 ‘13.1.2.4 Painful Trigeminal neuropathy attributed to MS plaque’: –described as ‘Trigeminal neuropathy induced by MS plaque’. attributed  induced reference to pain missing in the description 63

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 Institute for Healthcare Informatics L1- / L3 and IASP definition of pain IASP definition for ‘pain’: –‘an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage’; what asserts: –a common phenomenology (‘unpleasant sensory and emotional experience’) to all instances of pain, –the recognition of three distinct subtypes of pain involving, respectively: 1.actual tissue damage, 2.what is called ‘potential tissue damage’, and 3.a description involving reference to tissue damage whether or not there is such damage.

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 Institute for Healthcare Informatics Five pain-related phenomena 65 Smith B, Ceusters W, Goldberg LJ, Ohrbach R. Towards an Ontology of Pain. In: Mitsu Okada (ed.), Proceedings of the Conference on Logic and Ontology, Tokyo: Keio University Press, February 2011:23-32. Is this account: metaphysical? ontological? terminological? epistemological?

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 Institute for Healthcare Informatics 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: observation (data-element) versus observing diagnosis versus making a diagnosis message versus transmitting a message 66

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 Institute for Healthcare Informatics Is pain a continuant or occurrent? Pain with concordant tissue damage =def.: (1) a bodily process in an organism S involving two integrated levels: (1a) activation of the nociceptive system including the pain-associated emotion-generating brain components of S, and (1b) a simultaneous sensory and aversive experience on the part of S that is (2) caused by damage to tissue in a region R of the body of the subject S, (3) experienced by S as being caused by this damage, (4) such as to involve an aversive reaction on the part of S directed towards that which is presumed by S to be causing this damage, (5) concordant with the tissue damage on both levels (1a) and (1b), and also (6) such that the sensory experience is sufficiently intense to communicate the presence of tissue damage to the subject. 67 Smith B, Ceusters W, Goldberg LJ, Ohrbach R. Towards an Ontology of Pain. In: Mitsu Okada (ed.), Proceedings of the Conference on Logic and Ontology, Tokyo: Keio University Press, February 2011:23-32.

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 Institute for Healthcare Informatics Is pain a continuant or occurrent? Pain with concordant tissue damage =def.: (1) a bodily process in an organism S involving two integrated levels: (1a) activation of the nociceptive system including the pain-associated emotion-generating brain components of S, and (1b) a simultaneous sensory and aversive experience on the part of S that is (2) caused by damage to tissue in a region R of the body of the subject S, (3) experienced by S as being caused by this damage, (4) such as to involve an aversive reaction on the part of S directed towards that which is presumed by S to be causing this damage, (5) concordant with the tissue damage on both levels (1a) and (1b), and also (6) such that the sensory experience is sufficiently intense to communicate the presence of tissue damage to the subject. 68 Smith B, Ceusters W, Goldberg LJ, Ohrbach R. Towards an Ontology of Pain. In: Mitsu Okada (ed.), Proceedings of the Conference on Logic and Ontology, Tokyo: Keio University Press, February 2011:23-32. Is this account: metaphysical? ontological? terminological? epistemological?

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 Institute for Healthcare Informatics Between ‘generic’ and ‘specific’ L1. First-order reality L2. Beliefs (knowledge) GenericSpecific DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MIGRAINE HEADACHE PERSON DISEASE PATHOLOGICAL STRUCTURE PAIN DRUG me my headache my migraine my doctor my doctor’s computer L3. Representation pain classificationEHR ICHDmy EHR Referent TrackingBasic Formal Ontology GenericSpecific 69

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 Institute for Healthcare Informatics Basic Formal Ontology: an upper ontology based on Ontological Realism 70

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 Institute for Healthcare Informatics A useful parallel: Alberti’s grid reality representation Ontological theory

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 Institute for Healthcare Informatics 72 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

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 Institute for Healthcare Informatics 73 The example to work (partially) out: ‘pain’ me my toothache human being instance- of at t organism Is_a pain instance-of my brain part-of at t my caries signaling neurotransmission participant-of at t 2 brain to generate pain dispositionprocess instance-of at t Is_a has-participant at t 2 is-realized- in at t 2 inheres-in at t my left lower wisdom tooth part-of at t my LLWT caries instance-of at t tooth disorder part-of at t 1 participant-of at t 2 instance-of instance-of at t instance-of at t 1 part-of

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 Institute for Healthcare Informatics 74 Particulars Individual entities that carry identity and preserve their identity over time me my toothache my brain my caries signaling to generate pain my left lower wisdom tooth my LLWT caries

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 Institute for Healthcare Informatics 75 Universals Entities which exist “in” the particulars amongst which there is a relation of similarity not found with other particulars human being organism pain neurotransmission braindispositionprocess tooth disorder

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 Institute for Healthcare Informatics 76 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

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 Institute for Healthcare Informatics 77 Particulars and Universals 77 me my toothache human being instance- of at t organism Is_a pain instance-of my brain my caries signaling neurotransmission brain to generate pain dispositionprocess instance-of at t Is_a my left lower wisdom tooth my LLWT caries instance-of at t tooth disorder instance-of instance-of at t instance-of at t 1

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 Institute for Healthcare Informatics 78 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

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 Institute for Healthcare Informatics Continuants and Occurrents 79 me my toothache human being instance- of at t organism Is_a pain instance-of my brain my caries signaling neurotransmission brain to generate pain dispositionprocess instance-of at t Is_a my left lower wisdom tooth my LLWT caries instance-of at t tooth disorder instance-of instance-of at t instance-of at t 1

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 Institute for Healthcare Informatics 80 Continuants 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”. me human being instance- of at t organism Is_a my brain brain to generate pain disposition instance-of at t my left lower wisdom tooth my LLWT caries tooth disorder instance-of at t instance-of at t 1

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 Institute for Healthcare Informatics 81 Continuants preserve identity while changing caterpillarbutterfly animal t human being living creature me child Instance-of in 1960 adult me Instance-of since 1980

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 Institute for Healthcare Informatics BFO 2.0 continuants 82

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 Institute for Healthcare Informatics 83 Occurrents Occurrents are changes. Occurrents unfold themselves during temporal phases. At any point in time, they exist only in part. my toothache pain instance-of my caries signaling neurotransmission process Is_a instance-of

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 Institute for Healthcare Informatics BFO 2.0 occurrents 84

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 Institute for Healthcare Informatics Not easy to understand for conceptualists ‘the distinction between continuants and occurrents does not account for the contrast between reversible [processes] and irreversible processes in biology, chemistry, computation, or quantum mechanics’,  compare with: the distinction between males and females does not account for the contrast between nuns and housewives. 85 Maojo V, Crespo J, Garcia-Remesal M, de la Igleasia D, Perez-Rey D, Kulikowski C. Biomedical Ontologies: Towards Scientific Debate. Methods Inf Med. 2011 March 21;50(3)

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 Institute for Healthcare Informatics Independent versus dependent 86 me my toothache human being instance- of at t organism Is_a pain instance-of my brain my caries signaling neurotransmission brain to generate pain dispositionprocess instance-of at t Is_a my left lower wisdom tooth my LLWT caries instance-of at t tooth disorder instance-of instance-of at t instance-of at t 1

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 Institute for Healthcare Informatics 87 Independent versus dependent Independent entities Do not require any other entity to exist to enable their own existence me my toothache human being instance- of at t organism Is_a pain instance-of my brain my caries signaling neurotransmission brain to generate pain dispositionprocess instance-of at t Is_a instance-of at t instance-of Dependent entities Require the existence of another entity for their existence

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 Institute for Healthcare Informatics 88 Independent versus dependent me my toothache human being instance- of at t organism Is_a pain instance-of my brain my caries signaling neurotransmission brain to generate pain dispositionprocess instance-of at t Is_a instance-of at t instance-of 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 Independent continuants Dependent continuants Occurrents (all dependent)

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 Institute for Healthcare Informatics 89 Dependent continuants Realized –Quality:redness (of blood) Realizable –Function:to flex (of knee joint) –Role:student –Power:boss –Disposition:brittleness (of a bone)

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 Institute for Healthcare Informatics 90 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

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 Institute for Healthcare Informatics Not easy to understand for conceptualists ‘How can cells or viruses be entirely independent entities, even within a controlled laboratory environment?’  shows not understanding what ‘ontological dependence’ means 91 Maojo V, Crespo J, Garcia-Remesal M, de la Igleasia D, Perez-Rey D, Kulikowski C. Biomedical Ontologies: Towards Scientific Debate. Methods Inf Med. 2011 March 21;50(3)

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 Institute for Healthcare Informatics 92 Unconstrained reasoning OWL-DL reasoning Sorts of relations U1U2 P1 P2 UtoU: isa, partOf, … PtoU: instanceOf, lacks, denotes… PtoP: partOf, denotes, subclassOf,…

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 Institute for Healthcare Informatics Part-of different for continuants and occurrents 93 me my toothache human being instance- of at t organism Is_a pain instance-of my brain part-of at t my caries signaling neurotransmission brain to generate pain dispositionprocess instance-of at t Is_a my left lower wisdom tooth part-of at t instance-of at t tooth instance-of instance-of at t part-of

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 Institute for Healthcare Informatics 94 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left lower wisdom tooth part-of at t tooth 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

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 Institute for Healthcare Informatics 95 Part-of can be generalized, … with care ! 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 ? 95 me human being Instance-of at t living creature Is_a my left lower wisdom tooth part-of at t tooth Instance-of at t

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 Institute for Healthcare Informatics Part-of can be generalized, … with care ! Horse teeth are not parts of human beings Extracted teeth are not parts of human beings ‘Canonical tooth is part of canonical human being’, but…, there are (very likely) no such particulars … 96 Part-of ? 96 me human being Instance-of at t living creature Is_a my left lower wisdom tooth part-of at t tooth Instance-of at t

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 Institute for Healthcare Informatics 97 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

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 Institute for Healthcare Informatics 98 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

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 Institute for Healthcare Informatics 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

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

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

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 Institute for Healthcare Informatics etiological processdisorderdiseasepathological process abnormal bodily featuressigns & symptomsinterpretive processdiagnosis producesbearsrealized_in producesparticipates_inrecognized_as produces Approach http://code.google.com/p/ogms/ 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. http://www.referent-tracking.com/RTU/sendfile/?file=AMIA-0075-T2009.pdf

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 Institute for Healthcare Informatics No conflation of diagnosis, disease, and disorder The disorder is thereThe diagnosis is here The disease is there

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 Institute for Healthcare Informatics 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

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 Institute for Healthcare Informatics 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)

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 Institute for Healthcare Informatics 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.

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 Institute for Healthcare Informatics 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)

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 Institute for Healthcare Informatics 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

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 Institute for Healthcare Informatics Referent Tracking 109

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 Institute for Healthcare Informatics Motivation for RT: clarity about referents Generic terms used to denote specific entities do not have enough referential capacity –Usually enough to convey that some specific entity is denoted, –Not enough to be clear about which one in particular. For many ‘important’ entities, unique identifiers are used: –UPS parcels –Patients in hospitals –VINs on cars –…

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 Institute for Healthcare Informatics explicit reference to the concrete individual entities relevant to the accurate description of some portion of reality,... Fundamental goals of ‘our’ Referent Tracking Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

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 Institute for Healthcare Informatics Method: numbers instead of words Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78. –Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity 78

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 Institute for Healthcare Informatics Use these identifiers in expressions using a language that acknowledges the structure of reality: e.g.: a yellow ball: then not : yellow(#1) and ball(#1) rather: #1: the ball#2: #1’s yellow Then still not: ball(#1) and yellow(#2) and hascolor(#1, #2) but rather: instance-of(#1, ball, since t1) instance-of(#2, yellow, since t2) inheres-in(#1, #2, since t2) Fundamental goals of ‘our’ Referent Tracking  Strong foundations in realism-based ontology

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 Institute for Healthcare Informatics The shift envisioned From: –‘this man is a 40 year old patient with molar caries’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf caries… this-4 partOf this-5 … this-5 instanceOf molar… this-5 partOf this-1 … …

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 Institute for Healthcare Informatics The shift envisioned denotators for particulars From: –‘this man is a 40 year old patient with molar caries’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf caries… this-4 partOf this-5 … this-5 instanceOf molar… this-5 partOf this-1 … …

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 Institute for Healthcare Informatics The shift envisioned denotators for appropriate relations From: –‘this man is a 40 year old patient with molar caries’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf caries… this-4 partOf this-5 … this-5 instanceOf molar… this-5 partOf this-1 … …

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 Institute for Healthcare Informatics The shift envisioned denotators for universals or particulars From: –‘this man is a 40 year old patient with molar caries’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf caries… this-4 partOf this-5 … this-5 instanceOf molar… this-5 partOf this-1 … …

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 Institute for Healthcare Informatics The shift envisioned time stamp in case of continuants From: –‘this man is a 40 year old patient with molar caries’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years… this-2 qualityOf this-1… this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf caries… this-4 partOf this-5 … this-5 instanceOf molar… this-5 partOf this-1 … …

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 Institute for Healthcare Informatics Relevance: the way RT-compatible EHRs ought to interact with representations of generic portions of reality instance-of at t #105 caused by

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 Institute for Healthcare Informatics Integrating clinical datasets about orofacial pain 120

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 Institute for Healthcare Informatics Acknowledgement The work described is funded in part by grant 1R01DE021917-01A1 from the National Institute of Dental and Craniofacial Research (NIDCR). The content of this presentation is solely the responsibility of the author and does not necessarily represent the official views of the NIDCR or the National Institutes of Health. 121

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 Institute for Healthcare Informatics Collaborators 122 Werner Ceusters – Richard Ohrbach UB (PIs) Mike T. John – Eric L. Schiffman University of Minnesota Vishar Aggarwal Manchester, UK Joanna Zakrzewska London, UK Thomas List Malmö, Sweden Rafael Benoliel Hadassah, Israel

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 Institute for Healthcare Informatics Background (1) July 2008, Toronto: –the International RDC/TMD Consortium Network identified a need to incorporate the RDC/TMD diagnostic taxonomy into a comprehensive orofacial pain taxonomy. April, 2009, Miami: –‘The International Consensus Workshop: Convergence on an Orofacial Pain Taxonomy’ participants decided that an adequate treatment of the ontology of pain in general, and orofacial pain in particular, together with an appropriate terminology, is mandatory to advance the state of the art in diagnosis, treatment and prevention. 123 Ohrbach R, List T, Goulet J, Svensson P. Recommendations from the International Consensus Workshop: Convergence on an Orofacial Pain Taxonomy. Journal of Oral Rehabilitation. 2010.

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 Institute for Healthcare Informatics Background (2) The following consecutive steps were proposed: 1.study the terminology and ontology of pain as currently defined, 2.find ways to make individual data collections more useful for international research, 3.develop an ontology for integrating knowledge and data over all the known basic and clinical science domains concerning TMD and its relationship to complex disorders, and 4.expand this ontology to cover all pain-related disorders. 124

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 Institute for Healthcare Informatics Specific Aims 1.describe the portions of reality covered by the five datasets by means of a realism-based ontology (OPMQoL), 2.design bridging axioms required to express the data dictionaries of the datasets in terms of the OPMQoL and translate these axioms in the query languages used by the underlying databases, 3.validate OPMQoL by querying the datasets with and without using the ontology and by comparing the results in function of the clinical question identified, 4.document the development and validation approach in a way that other groups can re-use and expand OPMQoL, and use our approach in other domains. 125

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 Institute for Healthcare Informatics Considered datasets ‘US Dataset’ (724 patients) resulted from the NIH funded RDC/TMD Validation Project, ‘Hadassah Dataset’ (306 patients) from the Orofacial Pain Clinic at the Faculty of Dentistry, Hadassah, ‘German Dataset’ (416 patients) of patients seeking treatment for orofacial pain at the Department of Prosthodontics and Materials Sciences, University of Leipzig, ‘Swedish Dataset’of 46 consecutive Atypical Odontalgia (AO) patients recruited from 4 orofacial pain clinics in Sweden as well as data about age- and gender-matched control patients, 35 of which being painless and 41 being TMD patients, ‘UK Dataset’ (168 patients) of facial pain of non dental origin present for a minimum of three months. 126

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 Institute for Healthcare Informatics Challenges (1) which terms used in the domain correspond with real entities, (2) what real entities need to exist for certain signs and symptoms to manifest themselves, (3) to what degree do distinct pain disorders lead to similar types of signs and symptoms, and (4) to what extent can individual patients be suffering from distinct pain disorders at the same time, yet exhibiting manifestations that can be explained by the presence of only one particular pain disorder. 127

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 Institute for Healthcare Informatics Mapping assessment instrument terms, ontology and patient cases 128

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

130. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics The positive effects of appropriate mappings

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

132. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Linking the instruments and other tools analyze data dictionaries, assessment instruments, study criteria and corresponding terminologies, build realism-based application ontologies to link these sources to realism-based reference ontologies.

133. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Example: assessing TMJ Anatomy

134. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Panoramic X-ray of mouth

135. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Radiology RDC/TMD Examination: data collection sheet

136. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics RDC/TMD: a collaborator’s data dictionary Fieldnames in that collaborator’s data collection Allowed values for the fields

137. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Anybody sees something disturbing ?

138. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics This data dictionary alone is not reliable! That these variables are about the condylar head of the TMJ is ‘lost in translation’!

139. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics ‘meaning’ of values in data collections ‘The patient with patient identifier ‘PtID4’ is stated to have had a panoramic X-ray of the mouth which is interpreted to show subcortical sclerosis of that patient’s condylar head of the right temporomandibular joint’ 1 meaning

140. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Objectives of the ‘sources’ analysis Find for each value V in the data collections all possible configurations of entities (according to our best scientific understanding) for which the following can be true: – V –‘it is stated that V’ Describe these possible configurations by means of sentences from a formal language that mimic the structure of reality.

141. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Objectives of the ‘sources’ analysis (2) For example, –for the value stating that ‘The patient with patient identifier ‘PtID4’ has had a panoramic X-ray of the mouth which is interpreted to show subcortical sclerosis of that patient’s condylar head of the right temporomandibular joint’ to be true, –this statement must have been made, –for the statement to be true, there must have been that patient, an X-ray, etc, … –BUT! It is not necessarily true that that patient has indeed the sclerosis as diagnosed.

142. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Methodology (1): for the 1 st order reality 1.Formulate for each variable in the data collection a sentence explaining as accurately as possible what the variable stands for, 2.list the entities in reality that the terms in the sentence denote, 3.list recursively for all entities listed further entities that ontologically must exist for the entity under scrutiny to exist, 4.classify all entities in terms of realism-based ontologies (RBO), 5.specify all obtaining relationships between these entities, 6.outline all possible configurations of such entities for the sentence to be true.

143. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Step 1: formulate a statement ‘The patient with patient identifier ‘PtID4’ is stated to have had a panoramic X-ray of the mouth which is interpreted to show subcortical sclerosis of that patient’s condylar head of the right temporomandibular joint’ 1 meaning

144. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Step 2 (1): list the entities denoted 1(The patient) with 2(patient identifier ‘PtID4’) 3(is stated) 4(to have had) a 5(panoramic X-ray) of 6(the mouth) which 7(is interpreted) to 8(show) 9(subcortical sclerosis of 10(that patient’s condylar head of the 11(right temporomandibular joint)))’ CLASSINSTANCE IDENTIFIER personIUI-1 patient identifierIUI-2 assertionIUI-3 technically investigatingIUI-4 panoramic X-rayIUI-5 mouthIUI-6 interpretingIUI-7 seeingIUI-8 diagnosisIUI-9 condylar head of right TMJIUI-10 right TMJIUI-11 notes: colors have no meaning here, just provide easy reference, this first list can be different, any such differences being resolved in step 3

145. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Step 2 (2): provide directly referential descriptions CLASS INSTANCE IDENTIFIERDIRECTLY REFERENTIAL DESCRIPTIONS personIUI-1 the person to whom IUI-2 is assigned patient identifierIUI-2 the patient identifier of IUI-1 assertionIUI-3 'the patient with patient identifier PtID4 has had a panoramic X-ray of the mouth which is interpreted to show subcortical sclerosis of that patient’s right temporomandibular joint' technically investigatingIUI-4the technically investigating of IUI-6 panoramic X-rayIUI-5the panoramic X-ray that resulted from IUI-4 mouthIUI-6the mouth of IUI-1 interpretingIUI-7the interpreting of the signs exhibited by IUI-5 seeingIUI-8the seeing of IUI-5 which led to IUI-7 diagnosisIUI-9the diagnosis expressed by means of IUI-3 condylar head of right TMJIUI-10the condylar head of the right TMJ of IUI-1 right TMJIUI-11the right TMJ of IUI-1

146. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Step 3: identify further entities that ontologically must exist for each entity under scrutiny to exist. assigner roleIUI-12the assigner role played by the entity while it performed IUI-21 assigningIUI-21the assigning of IUI-2 to IUI-1 by the entity with role IUI-12 assertingIUI-20the asserting of IUI-3 by the entity with asserter role IUI-13 asserter roleIUI-13the asserter role played by the entity while it performed IUI-20 investigator roleIUI-14the investigator role played by the entity while it performed IUI-4 panoramic X-ray machine IUI-15the panoramic X-ray machine used for performing IUI-4 image bearerIUI-16the image bearer in which IUI-5 is concretized and that participated in IUI-8 interpreter roleIUI-17the interpreter role played by the entity while it performed IUI-7 perceptor roleIUI-18the perceptor role played by the entity while it performed IUI-8 diagnostic criteriaIUI-19the diagnostic criteria used by the entity that performed IUI-7 to come to IUI-9 study subject roleIUI-22the study subject role which inheres in IUI-1

147. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Step 3: some remarks interpreter role, perceptor role, … –reference to roles rather than the entity in which the roles inhere because it may be the same entity and one should not assign several IUIs to the same entity each description follows similar principles as Aristotelian definitions but is about particulars rather than universals

148. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Step 4: classify all entities in terms of realism-based ontologies requires more ontological and philosophical skills than domain expertise or expertise with Protégé, not just term matching CLASSHIGHER CLASS personBFO: Object patient identifierIAO: Information Content Entity assertionIAO: Information Content Entity technically investigating OBI: Assay panoramic X-rayIAO: Image mouthFMA: Mouth interpretingMFO: Assessing seeingBFO: Process diagnosisIAO: Information Content Entity condylar head of right TMJ FMA: Right condylar process of mandible right TMJFMA: Right temporomandibular joint assigner roleBFO: Role assigningBFO: Process study subject roleOBI: Study subject role

149. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Step 5: specify relationships between these entities For instance: –at least during the taking of the X-ray the study subject role inheres in the patient being investigated: IUI-23 inheres-in IUI-1 during t1 –the patient participates at that time in the investigation IUI-4 has-participant IUI-1 during t1 These relations need to follow the principles of the Relation Ontology. Smith B, Ceusters W, Klagges B, Koehler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector A, Rosse C. Relations in biomedical ontologies, Genome Biology 2005, 6:R46. Relations in biomedical ontologies

150. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Step 6: outline all possible configurations of such entities for the sentence to be true (a one semester course on its own) Such outlines are collections of relational expressions of the sort just described, Variant configurations for the example: –perceptor and interpreter are the same or distinct human beings, –the X-ray machine is unreliable and produced artifacts which the interpreter thought to be signs motivating his diagnosis, while the patient has indeed the disorder specified by the diagnosis (the clinician was lucky) –…

151. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Methodology (2): for each dataset Build a formal template which describes: –the results of steps 4-6 of the 1 st order analysis, –the relationships between: the 1 st order entities and the corresponding data items in the data set, data items themselves. Build a prototype able to generate on the basis of the template for each subject (patient) in the dataset an RT-compatible representation of his 1 st and 2 nd order entities. 151

152. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Partial Template for 3 variables (in the ‘German’ dataset) 152 RNVarRTREFMinMaxVal 1IMpatient_study_record 2idLVpatient_identifier 3idIMpatient 4sexCVgender 5sexCVmale0 6sexCVfemale1 7sexUAsexBLANK 8q3CVno_pain_in_ lower_face0 9q3CVpain_in_ lower_face1 10q3IMin_the_past_month 11q3IMlower_face 12q3IMtime_of_q3_concretization 13q3RPan_8_gcps_1000 14q3UPan_8_gcps_11100 15q3UAan_8_gcps_1BLANK 1 16q3JAan_8_gcps_1BLANK 0

153. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics 3 variables in the ‘German’ dataset 153 RNVarRTREFMinMaxVal 1IMpatient_study_record 2idLVpatient_identifier 3idIMpatient 4sexCVgender 5sexCVmale0 6sexCVfemale1 7sexUAsexBLANK 8q3CVno_pain_in_ lower_face0 9q3CVpain_in_ lower_face1 10q3IMin_the_past_month 11q3IMlower_face 12q3IMtime_of_q3_concretization 13q3RPan_8_gcps_1000 14q3UPan_8_gcps_11100 15q3UAan_8_gcps_1BLANK 1 16q3JAan_8_gcps_1BLANK 0 Answer to the question: ‘Have you had pain in the face, jaw, temple, in front of the ear or in the ear in the past month?’ Answer to the question: ‘’ How would you rate your facial pain on a 0 to 10 scale at the present time, that is right now, where 0 is "no pain" and 10 is "pain as bad as could be"?

154. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Record Types in the template 154 RNVarRTREFMinMaxVal 1IMpatient_study_record 2idLVpatient_identifier 3idIMpatient 4sexCVgender 5sexCVmale0 6sexCVfemale1 7sexUAsexBLANK 8q3CVno_pain_in_ lower_face0 9q3CVpain_in_ lower_face1 10q3IMin_the_past_month 11q3IMlower_face 12q3IMtime_of_q3_concretization 13q3RPan_8_gcps_1000 14q3UPan_8_gcps_11100 15q3UAan_8_gcps_1BLANK 1 16q3JAan_8_gcps_1BLANK 0 LV: Literal value CV: Coded Value IM: Implicit JA: Justified Absence UA: Unjustified Absence UP: Unjustified Presence RP: Redundant Presence

155. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Condition-based xA/xP determination 155 RNVarRTREFMinMaxVal 7sexUAsexBLANK 13q3RPan_8_gcps_1000 14q3UPan_8_gcps_11100 15q3UAan_8_gcps_1BLANK 1 16q3JAan_8_gcps_1BLANK 0 If the value of REF is either outside the range of Min/Max or ‘BLANK’ and the value for Var is as indicated by Val, including no value at all, then the presence or absence of the corresponding data item is of a sort indicated by RT.

156. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics RT compatible part of the template 156 RNIUI(L)IUI(P)P-TypeP-RelP-TargTrelTime 1#psrec- DATASET - RECORD att 2#pidL-#pid- DENOTATOR denotes#pat-att 3#patL-#pat- PATIENT att 4#patgL-#patg- GENDER inheres-in#pat-att 5#patg- MALE - GENDER inheres-in#pat-att 6#patg- FEMALE - GENDER inheres-in#pat-att 7#patgL- UNDERSPEC - ICE att 8#q3L0-#pat-lacks-pcp PAIN at#tq3- 9#q3L1-#pq3- PAIN participant#pat-at#tq3- 10#tq3- MONTH - PERIOD 11#patlf- LOWER - FACE part-of#pat-att 12#cq3- TIME - PERIOD after#tq3- 13#q3L- corresp-w#q3L0-att 14#q3L- DISINFORMATION att 15#q3L- UNDERSPEC - ICE att 16#q3L- J - BLANK - ICE att

157. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Conclusion Realism-based ontology has a lot to offer to make data collections comparable and unambiguously understandable. It is hard ! How far one needs to go depends on the purposes. –ideally: an analysis should be such that it can accommodate ALL purposes, i.e. the analysis should be independent of any purpose; distinction between reference ontologies and application ontologies.