1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U CHSS Data Center Work Weekend Ontology, Terminology, and Cardiovascular Surgery Nov 21, 2008 – Toronto, Canada Werner CEUSTERS, MD Center of Excellence in Bioinformatics and Life Sciences, and National Center for Biomedical Ontology, University at Buffalo, NY, USA

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Short personal history 1959 - 2008 1977 1989 1992 1998 2002 2004 2006 1993 1995

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Structure of this presentation Data and where they (should) come from Realism-based ontology Referent Tracking How to build ontologies from terminologies How to link to patient data How can disparate views been accommodated

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The central hypothesis For disease registries to facilitate meaningful multi-institutional outcomes analysis, there must be: 1.Common language = nomenclature, 2.Mechanism of data collection (database or registry) with an established uniform core data set, 3.Mechanism of evaluating case complexity, 4.Mechanism to ensure and verify data completeness and accuracy, 5.Collaboration between medical subspecialties. JP Jacobs et.al. Nomenclature and Databases — The Past, the Present, and the Future: A Primer for the Congenital Heart Surgeon. Pediatr Cardiol (2007)

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Would this do ? For disease registries to facilitate meaningful multi-institutional outcomes analysis, there must be: 1.Whatever sort of Common language = nomenclature, 2.Whatever sort of Mechanism of data collection (database or registry) with an established uniform core data set, 3.Whatever sort of Mechanism of evaluating case complexity, 4.Whatever sort of Mechanism to ensure and verify data completeness and accuracy, 5.Whatever sort of Collaboration between medical subspecialties. ?

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The answer is clearly … … No ! There are –many such animals –of various sorts, –which all have shortcomings, –and therefore lead to the creation of even more such animals, –which finally end up suffering – more or less - from the same flaws.

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Alagille Syndrome Aortic Coarctation Arrhythmogenic RV Dysplasia Cor Triatriatum... Aortic Coarctation Arrhythmogenic RV Dysplasia Cor Triatriatum... Alagille Syndrome Aortic Coarctation Arrhythmogenic RV Dysplasia Cor Triatriatum... Mesh 2008: congenital heart defects All MeSH Categories Diseases Category Cardiovascular Diseases Cardiovascular Abnormalities Heart Defects, Congenital All MeSH Categories Diseases Category Congenital, Hereditary, and Neonatal Diseases and Abnormalities Congenital Abnormalities Cardiovascular Abnormalities Heart Defects, Congenital All MeSH Categories Diseases Category Cardiovascular Diseases Heart Diseases Heart Defects, Congenital ?

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED-CT version 2008.01.7AC

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED-CT’s ‘Fallot’s trilogy’ versus ‘Fallot’s triad’

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Trilogy of Fallot Definition: –Combination of pulmonary valve stenosis and atrial septal defect with right ventricular hypertrophy. Typical representational mistake: –From (correctly, if the definition is right) : ‘a patient which has Fallot’s triad –has a pulmonary valve stenosis, –has an atrial septal defect, –has a right ventricular hypertrophy.’ –To (wrong, even if the definition is right) : ‘a Fallot’s triad –is a pulmonary valve stenosis, –is an atrial septal defect, –is a right ventricular hypertrophy.’

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U In general: some alarming publications 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,

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Key question: Why is this ?

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘The spectrum of the Health Sciences’ http://www.uvm.edu/~ccts Turning data in knowledge

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What is missing here ? http://www.uvm.edu/~ccts ? Turning data in knowledge

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Source of all data Reality !

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

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Key components data information knowledge hypotheses Players HIT Outcomes generates influences representation reality about

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Current deficiencies At the level of reality: –Desired outcomes different for distinct players Competing interests –Multitude of HIT applications and paradigms At the level of representations: –Variety of formats –Silo formation –Doubtful semantics In their interplay: –Very poor provenance or history keeping –No formal link with that what the data are about –Low quality

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Where should we go?

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ultimate goal (at least mine) A digital copy of the world

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Requirements for this digital copy R1:A faithful representation of reality R2… of everything that is digitally registered, what is generic  scientific theories what is specific  what individual entities exist and how they relate R3:… throughout reality’s entire history, R4… which is computable in order to … … allow queries over the world’s past and present, … make predictions, … fill in gaps, … identify mistakes,...

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U In fact … the ultimate crystal ball

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The ‘binding’ wall How to do it right ? A cartoon of the world

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Major problems 1.A mismatch between what is - and has been - the case in reality, and representations thereof in: a)(generic) Knowledge repositories, and b)(specific) Data and Information repositories. 2.An inadequate integration of a) and b). Solutions Philosophical realism Realism-based Ontology Referent Tracking PhilosophyHITPhilosophyHIT

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Realism-based Ontology

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

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

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U However: the dominant view in Comp Sc is conceptualism Semantic Triangle concept objectterm Embedded in Terminology

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The concept-based view PPPP PPPP PPPP isa class

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The realism-based view PPPP PPPP PPPP universal instance-of extension-of member-of class Defined class e.g. human e.g. all humans e.g. all humans in this room

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

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

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

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Cardiovascular surgery examples Systemic venous anomaly, SVC, Bilateral SVC Systemic venous anomaly, SVC, Bilateral SVC, Innominate absent Systemic venous anomaly, SVC, Bilateral SVC, Innominate present VA valve overriding VA valve overriding, Aortic valve VA valve overriding, Left sided VA Valve VA valve overriding, Pulmonary valve VA valve overriding, Right sided VA Valve VA valve overriding-modifier for degree of override, Override of VA valve,50% VA valve overriding-modifier for degree of override, Override of VA valve.90% VA valve overriding-modifier for degree of override, Override of VA valve 50–90% JP. Jacobs et.al. The nomenclature, definition and classification of cardiac structures in the setting of heterotaxy. Cardiol Young 2007; 17(Suppl. 2): 1–28

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

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

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

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

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

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The three levels in medical practice 1. First-order reality 2. Beliefs (knowledge) GenericSpecific DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MOLECULE PERSON DISEASE PATHOLOGICAL STRUCTURE BLOOD PRESSURE DRUG me my blood pressure my ASD my doctor my doctor’s computer 3. Representation ‘atrial septal defect’‘W. Ceusters’‘my heart defect’

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

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

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

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

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

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

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representations for portions of reality Level 1 Level 2 or 3 Level 3 47

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

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

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Requirements for a digital copy of the world R1:A faithful representation of reality R2… of everything that is digitally registered, what is generic  scientific theories  realism-based ontologies what is specific  what individual entities exist and how they relate R3:… throughout reality’s entire history, R4… which is computable in order to … … allow queries over the world’s past and present, … make predictions, … fill in gaps, … identify mistakes,...

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The reality: a digital copy of part of the world Applying the grid should not give a distorted representation of reality, but only an incomplete representation !!!

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Key issue: keeping track of what the bits denote

53. New York State Center of Excellence in Bioinformatics & Life Sciences R T U explicit reference to the concrete individual entities relevant to the accurate description of each patient’s condition, therapies, outcomes,... Fundamental goal of Referent Tracking Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

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

55. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The essence of Referent Tracking Keeping track of particulars By means of singular and globally unique identifiers (#1, #2, #3, …) That function as surrogates for these entities in information systems, documents, etc And are managed IN a referent tracking system. Ceusters W. and Smith B. Tracking Referents in Electronic Health Records. In: Engelbrecht R. et al. (eds.) Medical Informatics Europe, IOS Press, Amsterdam, 2005;:71-76

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

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

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

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

60. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Requirements for a digital copy of the world R1:A faithful representation of reality R2… of everything that is digitally registered, what is generic  scientific theories what is specific  what individual entities exist and how they relate R3:… throughout reality’s entire history, R4… which is computable in order to … … allow queries over the world’s past and present, … make predictions, … fill in gaps, … identify mistakes,...

61. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Eternal memory

62. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Accept that everything may change: 1.changes in the underlying reality: Particulars come, change and go

63. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Identity & instantiation childadult caterpillar butterfly t person animal Living creature

64. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Accept that everything may change: 1.changes in the underlying reality: Particulars come, change and go 2.changes in our (scientific) understanding: The plant Vulcan does not exist

65. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality and representation: both in evolution IUI-#3 O-#2: ‘cancer’ O-#1: ‘benign tumor’ t U1: benign tumor U2: malignant tumor p3 Reality Repr. O-#0: diabolic possession = “denotes” = what constitutes the meaning of representational units …. Therefore: O-#0 is meaningless

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

67. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Changes over time In John Smith’s Electronic Health Record: –At t 1 : “male”at t 2 : “female” What are the possibilities ? Change in reality: transgender surgery change in legal self-identification Change in understanding: it was female from the very beginning but interpreted wrongly Correction of data entry mistake: it was understood as male, but wrongly transcribed (Change in word meaning)

68. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Requirements for a digital copy of the world R1:A faithful representation of reality R2… of everything that is digitally registered, what is generic  scientific theories what is specific  what individual entities exist and how they relate R3:… throughout reality’s entire history, R4… which is computable in order to … … allow queries over the world’s past and present, … make predictions, … fill in gaps, … identify mistakes,...

69. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking System Components Referent Tracking Software Manipulation of statements about facts and beliefs Referent Tracking Datastore: IUI repository A collection of globally unique singular identifiers denoting particulars Referent Tracking Database A collection of facts and beliefs about the particulars denoted in the IUI repository Manzoor S, Ceusters W, Rudnicki R. Implementation of a Referent Tracking System. International Journal of Healthcare Information Systems and Informatics 2007;2(4):41-58.

70. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Place in the Health IT arena

71. New York State Center of Excellence in Bioinformatics & Life Sciences R T U How to build an ontology from a terminology?

72. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Steps in ontology building 1.For all terms identified in the terminology, find the entities in reality that are directly denoted; 2.Determine the top categories these entities belong to; 3.Determine for any dependent entity: If process: the continuants that participate in it If dependent continuant: the continuant upon which it depends 4.For any entity determined in step 3, go to step 2. Rudnicki R, Ceusters W, Manzoor S, Smith B. What Particulars are Referred to in EHR Data? A Case Study in Integrating Referent Tracking into an Electronic Health Record Application. In Teich JM, Suermondt J, Hripcsak C. (eds.), American Medical Informatics Association 2007 Annual Symposium Proceedings, Biomedical and Health Informatics: From Foundations to Applications to Policy, Chicago IL, 2007;:630-634.

73. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Building the Ontology underlying a terminology (MDS) MDS Ontology U2U2 U3U3 U5U5 U4U4 U6U6 MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … U 11 U7U7 U 14 U 13 U 10 U 12 MDS terms U 17 U 16 U1U1 U9U9 U8U8 BFO Class-relations

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

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

76. New York State Center of Excellence in Bioinformatics & Life Sciences R T U How to link to patient data ?

77. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Semantic integration of data expressed in distinct terminologies Purpose: –Better comparability –Statistical validation of the ontology Explanation of observed correlations between assessment data elements Finding patient subpopulations exhibiting correlations which are near- significant without the ontology, but significant with the ontology Two level integration: –Type level : poor man’s linkage –Particular level: rich man’s linkage

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

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

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

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

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

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

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

85. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What has worked ? How have disparate views been accommodated?

86. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Definitions for ‘Adverse Event’ D4an observation of a change in the state of a subject assessed as being untoward by one or more interested parties within the context of a protocol-driven research or public health. BRIDG D5an event that results in unintended harm to the patient by an act of commission or omission rather than by the underlying disease or condition of the patient IOM D6any unfavorable and unintended sign (including an abnormal laboratory finding), symptom, or disease temporally associated with the use of a medical treatment or procedure that may or may not be considered related to the medical treatment or procedure NCI D7any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not necessarily have to have a causal relationship with this treatment CDISC D8an untoward, undesirable, and usually unanticipated event, such as death of a patient, an employee, or a visitor in a health care organization. Incidents such as patient falls or improper administration of medications are also considered adverse events even if there is no permanent effect on the patient. JTC D9an injury that was caused by medical management and that results in measurable disability. QUIC

87. New York State Center of Excellence in Bioinformatics & Life Sciences R T U At least one argument There is no entity which would be such that, were it placed before these authors, they would each in turn be able to point to it and respectively say – faithfully and honestly – –“that is an observation” (definition D4), –“that is an injury” (definition D9), –“that is a laboratory finding” (definition D6). Clearly, –nothing which is an injury can be a laboratory finding, although, of course, laboratory findings can aid in diagnosing an injury or in monitoring its evolution. –nothing which is a laboratory finding, can be an observation, although, of course, some observation must have been made if we are to arrive at a laboratory finding.

88. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Hypothesis Because … –all the authors of the mentioned definitions use the term ‘adverse event’ in some context for a variety of distinct entities, and –these contexts look quite similar in each of them, more or less the same sort of entities seem to be involved … there is some common ground (some portion of reality) which is such that the entities within it can be used as referents for the various meanings of ‘adverse event’.

89. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Why does this matter ? Be precise about what representational units in either an ontology or data repository stand for. Each such unit in an ontology should come with additional information on whether it denotes: –an entity at level 1, level 2 or level 3 and –a universal, or a defined or composite class

90. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Examples from our adverse event domain ontology DenotationClass TypeParticular TypeDescription (role in adverse event scenario) Level 1 C1subject of careDCindependent continuant person to whom harm might have been done through an act under scrutiny C2act under scrutinyDCact of careact of care that might have caused harm to the subject of care C7structure changeUprocesschange in an anatomical structure of a person C8structure integrityUdependent continuant aspect of an anatomical structure deviation from which would bring it about that the anatomical structure would either (1) itself become dysfunctional or (2) cause dysfunction in another anatomical structure C12subject investigation DCprocesslooking for a structure change in the subject of care Level 2 C15observationDCdependent continuant cognitive representation of a structure change resulting from an act of perception within a subject investigation C16harm diagnosisDCdependent continuant cognitive representation, resulting from a harm assessment, and involving an assertion to the effect that a structure change is or is not a harm Level 3 C18care referenceDCinformation entity concretized (through text, diagram, …) piece of knowledge drawn from state of the art principles that can be used to support the appropriateness of (or correctness with which) processes are performed involving a subject of care

91. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representing particular cases Is the generic representation of the portion of reality adequate enough for the description of particular cases? Example: a patient –born at time t0 –undergoing anti-inflammatory treatment and physiotherapy since t2 –for an arthrosis present since t1 –develops a stomach ulcer at t3.

92. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Anti-inflammatory treatment with ulcer development IUIParticular descriptionProperties #1the patient who is treated#1 member C1 since t 2 #2#1’s treatment#2 instance_of C3 #2 has_participant #1 since t 2 #2 has_agent #3 since t 2 #3the physician responsible for #2#3 member C4 since t 2 #4#1’s arthrosis#4 member C5 since t 1 #5#1’s anti-inflammatory treatment#5 part_of #2 #5 member C2 since t 3 #6#1’s physiotherapy#6 part_of #2 #7#1’s stomach#7 member C6 since t 2 #8#7’s structure integrity#8 instance_of C8 since t 0 #8 inheres_in #7 since t 0 #9#1’s stomach ulcer#9 part_of #7 since t 3 #10coming into existence of #9#10 has_participant #9 at t 3 #11change brought about by #9#11 has_agent #9 since t 3 #11 has_participant #8 since t 3 #11 instance_of C10 at t 3 #12noticing the presence of #9#12 has_participant #9 at t 3+x #12 has_agent #3 at t 3+x #13cognitive representation in #3 about #9#13 is_about #9 since t 3+x

93. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Advantage 1: reduce ambiguity in definitions E.g. ‘adverse drug reaction: an undesirable response associated with use of a drug that either compromises therapeutic efficacy, enhances toxicity, or both.’ (Joint Technical Committee) –May denote something on level 1, e.g. a realizable entity which exists objectively as an increased health risk; in this sense any event ‘that either compromises therapeutic efficacy, enhances toxicity, or both’ is undesirable; –May denote something on level 2, so that, amongst all of those events which influence therapeutic efficacy or toxicity, only some are considered undesirable (for whatever reason) by either the patient, the caregiver or both; or –May denote something relating to level 3, so a particular event occurring on level 1 is undesirable only when it is an instance of a type of event that is listed in some guideline, good practice management handbook, i.e. in some published statement of the state of the art in relevant matters.

94. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Advantage 2: reveal hidden assumptions E.g.: ‘adverse event: an event that results in unintended harm to the patient by an act of commission or omission rather than by the underlying disease or condition of the patient’ (IOM) But: –An ‘act of omission’ is under the realist agenda not an entity that exist at level 1, but rather a level 3 entity denoting a configuration in which not was done what good practice requires to be done, –Something what not exist at level 1, cannot cause harm by itself, –Thus it must be the underlying disease.

95. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusion Health data management involves many actors and IT systems: semantic interoperability is thus a key issue. Ontologies (of the right sort) provide a deep level of semantic interoperability between IT systems, thereby keeping track: –of what is the case; –of what is known by some actor(s); –of what has been and still needs to be done. Realism-based ontology, as a discipline, helps in creating ontologies of the right sort.