1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ICBO Tutorial Introduction to Referent Tracking July 22, 2009 112 Norton Hall, UB North Campus Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences Ontology Research Group University at Buffalo, NY, USA

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ? Short personal history 1959 - 2009 1977 1989 1992 1998 2002 2004 2006 1993 1995

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U House keeping rules Feel free to ask clarifications at any time if you don’t understand something I just said (but not more than three slides earlier) ; Please do not interrupt me if you ‘just’ disagree with something I say until: –near beginning of the break, –near end of the tutorial; Everybody in the audience may sleep except those students who are here for credit, –I’ll test them –redundancy in my slides serves thus a purpose: to help them !

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Tutorial overview Setting the scene: a rough description of what Referent Tracking is and why it is important Review the basics of BFO relevant to RT The crucial distinction between representations and what they represent Implementation of RT systems Examples of use

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Prologue: Referent Tracking: What and Why ?

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U When did Weiss kill Senator Long ? time Senator Long’s living Weiss’ shooting of Long Carl Weiss’ living Bodyguards’shooting of Weiss Weiss’s path. body reactions Long’s pathological body reactions

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What is Referent Tracking ? A paradigm under development since 2005, –based on Basic Formal Ontology, –designed to keep track of relevant portions of reality and what is believed and communicated about them, –enabling adequate use of realism-based ontologies, terminologies, thesauri, and vocabularies, –originally conceived to track particulars on the side of the patient and his environment denoted in his EHR, –but since then studied in and applied to a variety of domains, –and now evolving towards tracking absolutely everything, not only particulars, but also universals.

8. 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

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

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ultimate goal of Referent Tracking A digital copy of the world

11. 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,...

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

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The ‘binding’ wall How to do it right ? I don’t want a cartoon of the world

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Distinction between Ontologies and Information Models Ontologies should represent only what is always true about the entities of a domain (whether or not it is known to the person that reports), Information models (or data structures) should only represent the artifacts in which information is recorded. –Such information may be incomplete and error-laden which needs to be accounted for in the information model rather than in the ontology itself.

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Perfect ‘semantic’ tools are useless … … if data captured at the source is not of high quality Prevailing EHR systems don’t allow data to be stored at acceptable quality level: –No formal distinction between disorders and diagnosis –Messy nature of the notions of ‘problem’ and ‘concern’ –No unique identification of the entities about which data is stored Unique IDs for data-elements cannot serve as unique IDs for the entities denoted by these data-elements

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminologies for ‘unambiguous representation’ ??? 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateObsCodeNarrative 093920/12/1998255087006malignant polyp of biliary tract

17. 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 If two different fracture codes are used in relation to observations made on the same day for the same patient, do they denote the same fracture ? Terminologies for ‘unambiguous representation’ ???

18. 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 If the same fracture code is used for the same patient on different dates, can these codes denote the same fracture? Terminologies for ‘unambiguous representation’ ???

19. 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 Can the same fracture code used in relation to two different patients denote the same fracture? Terminologies for ‘unambiguous representation’ ???

20. 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 Can two different tumor codes used in relation to observations made on different dates for the same patient, denote the same tumor ? Terminologies for ‘unambiguous representation’ ???

21. 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 Do three references of ‘hypertension’ for the same patient denote three times the same disease? Terminologies for ‘unambiguous representation’ ???

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminologies for ‘unambiguous representation’ ??? 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateObsCodeNarrative 093920/12/1998255087006malignant polyp of biliary tract Can the same type of location code used in relation to three different events denote the same location?

23. 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 How will we ever know ?

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The problem in a nutshell 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 –…

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 1.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.

26. 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

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 2.Use these identifiers in expressions using a language that acknowledges the structure of reality e.g.: a yellow ball: #1: the ball#2: #1’s yellow Then not: ball(#1) and yellow(#2) and hascolor(#1, #2) But: instance-of(#1, ball, since t) instance-of(#2, yellow, since t) inheres-in(#1, #2, since t) Fundamental goals of ‘our’ Referent Tracking  Strong foundations in realism-based ontology

28. 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

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Principles for Success’ Evolutionary change Radical change: Principle 6: Architect Information and Workflow Systems to Accommodate Disruptive Change »Organizations should architect health care IT for flexibility to support disruptive change rather than to optimize today’s ideas about health care. Principle 7: Archive Data for Subsequent Re-interpretation »Vendors of health care IT should provide the capability of recording any data collected in their measured, uninterpreted, original form, archiving them as long as possible to enable subsequent retrospective views and analyses of those data. NOTE Willam W. Stead and Herbert S. Lin, editors; Committee on Engaging the Computer Science Research Community in Health Care Informatics; National Research Council. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions (2009)

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Principles for Success’ (continued) The NOTE: –‘See, for example, Werner Ceusters and Barry Smith, “Strategies for Referent Tracking in Electronic Health Records” Journal of Biomedical Informatics 39(3):362-378, June 2006.’ Willam W. Stead and Herbert S. Lin, editors; Committee on Engaging the Computer Science Research Community in Health Care Informatics; National Research Council. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions (2009)

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Words, words, words, … A paradigm under development since 2005, –based on Basic Formal Ontology, –designed to keep track of relevant portions of reality and what is believed and communicated about them, –enabling adequate use of realism-based ontologies, terminologies, thesauri, and vocabularies, –originally conceived to track particulars on the side of the patient and his environment denoted in his EHR, –but since then studied in and applied to a variety of domains, –and now evolving towards tracking absolutely everything, not only particulars, but also universals.

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Therefore: Part 1: the Basics No (good) Referent Tracking without (good) Realism-based Ontology

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 1.There is an external reality which is ‘objectively’ the way it is; 2.That reality is accessible to us; 3.We build in our brains cognitive representations of reality; 4.We communicate with others about what is there, and what we believe there is there. Basic axioms 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

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What is there ? The parts of BFO relevant for Referent Tracking (1) some particular some universal instanceOf …

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this man is a 40 year old patient with a stomach tumor’ 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 tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … …

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this man is a 40 year old patient with a stomach tumor’ 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 tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … denotators for particulars

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this man is a 40 year old patient with a stomach tumor’ 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 tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … denotators for appropriate relations

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this man is a 40 year old patient with a stomach tumor’ 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 tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … denotators for universals or particulars

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this man is a 40 year old patient with a stomach tumor’ 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 tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … something I’ll come to later

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relevance: the way RT-compatible systems ought to interact with representations of generic portions of reality instance-of at t #105 caused by

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What is there ? The parts of BFO relevant for Referent Tracking (1) 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

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U My terminology (1) ‘entity’: –denotes either a universal or a particular ‘instance’: –denotes a particular to which I refer in the context of some universal: If A instanceOf B … then –‘B is a universal’ –‘A is a particular’ –‘A is an instance’

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U My terminology (1) ‘entity’: –denotes either a universal or a particular ‘instance’: –denotes a particular to which I refer in the context of some universal: If A instanceOf B … then –‘B is a universal’ –‘A is a particular’ –‘A is an instance’ do not denote isa !!!

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U My terminology (2) ‘entity’: –denotes either a universal or a particular ‘instance’: –denotes a particular to which I refer in the context of some universal: If A instanceOf B … then –‘B is a universal’ –‘A is a particular’ –‘A is an instance’ ‘denotes’: (roughly for now) a relation between an entity and a representational construct (sign, symbol, term,…) such that the latter stands for the former in descriptions about reality.

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What is there ? The parts of BFO relevant for Referent Tracking (1) some particular some universal instanceOf … ?

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U instanceOf What is there ? The parts of BFO relevant for Referent Tracking (2) some continuant particular some continuant universal instanceOf at t some occurrent particular some occurrent universal

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

48. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Use of the CEN Time Standard for HIT

49. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Things do change indeed childadult caterpillar butterfly t person animal Living creature vampire

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The continuants relevant for Referent Tracking spatial region independent continuant generically dependent continuant specifically dependent continuant dependent continuant information content entity material object site ontologyterminology…

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U My terminology (3) ‘ontology’: –denotes an information artifact whose representational elements denote universals - either directly or indirectly - and whose structure is intended to mimic the structure of reality ‘terminology’: –denotes an information artifact whose representational elements are terms from some language that are defined in terms of other terms and that are structured independent of the structure of reality

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MeSH-2008: give me 666 reasons why this is not an ontology under my terminology. 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

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

54. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Snomed CT (July 2007): Why not an ontology ?

55. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Cause: coding / classification confusion ‘A patient with a fractured nasal bone’ ‘A patient with a broken nose’ ‘A patient with a fracture of the nose’ means the same thing as note: doesn’t say what it means

56. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = = Cause: coding / classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

57. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The occurrents relevant for Referent Tracking spatiotemporal region contiguous temporal region history process time instant time interval temporal region scattered temporal region

58. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Sorts of relations U1U2 P1 P2 UtoU: isa, partOf(UU), … PtoU: instanceOf, lacks, denotes(PU)… PtoP: partOf, denotes, …

59. New York State Center of Excellence in Bioinformatics & Life Sciences R T U tt t instanceOf Putting the pieces together: what is there to track? 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 t occupies projectsOn at t

60. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 2: Let’s get more serious about ‘representation’ (in general) Beware !!! Colors don’t really matter but in what follows I used them in different ways than before.

61. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Marriage’ marriage of Bill and Hillary Bill Clinton Hillary Clinton human being marriage husbandIn spouseIn husbandOf spouseOf instanceOf createdBy …

62. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Time and the Bill-Hillary marriage: what about the various some t’s ? marriage of Bill and Hillary Bill Clinton Hillary Clinton human being marriage husbandIn spouseIn husbandOf spouseOf instanceOf createdBy … exists at some t at some t

63. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representation of the Bill-Hillary marriage ‘marriage of Bill and Hillary’ ‘Bill Clinton’ ‘Hillary Clinton’ ‘human being’ ‘marriage’ husbandIn spouseIn husbandOf spouseOf instanceOf createdBy … exists at some t at some t ‘, ‘, ‘,

64. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representation and what it is about ? at some t

65. New York State Center of Excellence in Bioinformatics & Life Sciences R T U instanceOf Representations as first order entities (1) ?2 ?1 ?3 isa at some t

66. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representations as first order entities (2) ontology about L1 R

67. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Two sorts of representations L1 R L2L3 beliefs symbolizations ‘about’

68. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Three levels of reality

69. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Diseases : L1  Diagnoses L2/L3 Diagnosis: A configuration of representational units; Believed to mirror the person’s disease; Believed to mirror the disease’s cause; Refers to the universal of which the disease is believed to be an instance. #56 John’s Pneumonia #78 John’s portion of pneumococs Pneumococcal pneumonia caused by Instance-of at t1 Disease isa

70. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some motivations and consequences (1) The same diagnosis can be expressed in various forms. #56#78 Pneumococcal pneumonia caused by Instance-of at t1 #56#78 Pneumonia caused by Portion of pneumococs Instance-of at t1 Disease isa caused by

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

72. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Distinct but similar diagnoses #56 John’s Pneumonia #78 John’s portion of pneumococs Pneumococcal pneumonia caused by #956 Bob’s pneumonia #2087 Bob’s portion of pneumococs caused by Instance-of at t1Instance-of at t2

73. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some motivations and consequences (3) Allows evenly clean interpretations for the wealth of ‘modified’ diagnoses: –With respect to the author of the representation: ‘nursing diagnosis’, ‘referral diagnosis’ –When created: ‘post-operative diagnosis’, ‘admitting diagnosis’, ‘final diagnosis’ –Degree of the belief: ‘uncertain diagnosis’, ‘preliminary diagnosis’

74. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Important to differentiate between Lexical, semantic and ontological relations ‘gall’ ‘gallbladder ’ ‘urinary bladder’ ‘urine’ ‘urinary bladder inflammation’ ‘gallbladder inflammation’ ‘inflammation’ gall gall bladder bladder inflammation urine cystitis biliary cystitis gallbladder inflammation urinary bladder

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

76. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent TrackingBasic Formal Ontology The three levels applied to C2 1. First-order reality 2. Beliefs (knowledge) GenericSpecific GOAL ATTACK STRATEGY John Doe’s plan SACEUR’s strategy TANK PERSON CORPSE building SOLDIER WEAPON John Doe’s platoon Tank with serial number TH1280A44V John Doe’s gun Private John Doe 3. Representation ‘weapon’‘person’‘tank’‘John Doe’‘Enola Gay’

77. 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?

78. New York State Center of Excellence in Bioinformatics & Life Sciences R T U And often confuse L3 with L1 ‘Head’ in the NCIT

79. 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 ?

80. 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?

81. 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?

82. 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?

83. 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?

84. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Central mechanism in RT: ‘denotation’ Something like a marriage between an L3-entity and an L1-entity marriage of Bill and Hillary Bill Clinton Hillary Clinton husbandInspouseIn husbandOf spouseOf createdBy … this particular denotation ‘This green square’ hasReference referentOf denotes denotedBy createdBy … referenceOf hasReferent

85. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Denotation and time: some axioms this particular denotation ‘This green square’ hasReference at t1 referentOf at t1 denotes at t1 denotedBy at t1 createdBy … S1 referenceOf at t1 hasReferent at t1 D cannot exist if S or R never existed D can continue to exist even when S does not exist anymore the existence of R and S are not sufficient for D to exist D ceases to exist when R ceases to exist … D R S

86. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Denotators with distinct ‘meanings’ this particular denotation ‘This green square’ hasReference at t1 denotes at t1 createdBy at … S1 hasReferent at t1 A1 this other particular denotation S2 createdBy at … A2

87. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Changes in reality this particular denotation ‘This green square’ hasReference at t2 denotes at t2 createdBy at … S1 hasReferent at t2 A1 this other particular denotation S2 createdBy at … A2 S1 (imagine S1 turned red, yet still being that very same square on the very same spot) ‘at’ as defined in CEN:TSHSP thus t2 is the ‘coContinuation’ of t1

88. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Changes in representations representationOf at t

89. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality and representations representationOf at t 1 representationOf at t 2

90. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality and representations representationOf at t 1 representationOf at t 2 gain in understanding

91. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Changes in SNOMED

92. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality and representation: both in evolution IUI-#3 O-#2 O-#1 t U1 U2 p3 Reality Repr. O-#0 = “denotes” = what constitutes the meaning of representational units …. Therefore: O-#0 is meaningless

93. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality versus representations, both in evolution t U1 U2 p3 IUI-#3 O-#2 O-#1 L1 L2 O-#0 Several types of mismatches between reality and representations

94. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mistakes, discoveries, being lucky, having bad luck t U1 U2 p3 IUI-#3 O-#2 O-#1 O-#0 Mistakes L1 L2

95. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mistakes, discoveries, being lucky, having bad luck t U1 U2 p3 IUI-#3 O-#2 O-#1 O-#0 discoveries L1 L2

96. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mistakes, discoveries, being lucky, having bad luck t U1 U2 p3 IUI-#3 O-#2 O-#1 O-#0 L1 L2

97. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mistakes, discoveries, being lucky, having bad luck t U1 U2 p3 IUI-#3 O-#2 O-#1 O-#0 L1 L2

98. 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)

99. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 3: Representation in Referent Tracking

100. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representations in Referent Tracking Portion of Reality Entity Particular Universal Defined class Representation Non-referring particular Denotator IUI RT-tuple corresponds-to Configuration represents CUIUUI denotes is about Representational unit denotes contains class Extension … … … Relation RUI denotes Information content ent.

101. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Extensions – Defined Classes

102. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Further distinctions amongst PORs in RT

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

104. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Architecture of a Referent Tracking System (RTS) RTS: system in which all statements referring to particulars contain the IUIs for those particulars judged to be relevant. Ideally set up as broad as possible: – some metrics: % of particulars referred to by means of IUI % of HCs active in a region –Geographic region –functional region: defined by contacts amongst patients % of patients referred to within a region Services: –IUI generator –IUI repository: statements about assignments and reservations –Referent Tracking ‘Database’ (RTDB): index (LSID) to statements relating instances to instances and classes

105. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking System Components Referent Tracking Software Manipulation of assertions about L1 Referent Tracking Datastore: IUI repository A collection of globally unique singular identifiers denoting particulars Referent Tracking Database A collection of assertions 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.

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

107. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Elementary RTS tuple types (1.0)

108. New York State Center of Excellence in Bioinformatics & Life Sciences R T U IUI assignment = an act carried out by the first ‘cognitive agent’ feeling the need to acknowledge the existence of a particular it has information about by labeling it with a UUID. ‘cognitive agent’: –A person; –An organization; –A device or software agent, e.g. Bank note printer, Image analysis software.

109. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Criteria for IUI assignment (1) The particular’s existence must be determined: –Easy for persons in front of you, body parts,... –Easy for ‘planned acts’: they do not exist before the plan is executed ! Only the plan exists and possibly the statements made about the future execution of the plan –More difficult: subjective symptoms But the statements the patient makes about them do exist ! –However: no need to know what the particular exactly is, i.e. which universal it instantiates Not always a need to be able to point to it precisely –One bee out of a particular swarm that stung the patient, one pain out of a series of pain attacks that made the patient worried –But: this is not a matter of choice, not ‘any’ out of...

110. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Criteria for IUI assignment (2) May not have already been assigned a IUI. Morning star and evening star Himalaya Multiple sclerosis It must be relevant to do so: Personal decision, (scientific) community guideline,... Possibilities offered by the EHR system If a IUI has been assigned by somebody, everybody else making statements about the particular should use it

111. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Assertion of assignments IUI assignment is an act of which the execution has to be asserted in the IUI-repository: –D i = (1.0) IUI d IUI of the registering agent A i the assertion of the assignment »IUI a IUI of the author of the assertion »IUI p IUI of the particular »t ap time of the assignment t d time of registering A i in the IUI-repository Neither t d or t ap give any information about when # IUI p started to exist ! That might be asserted in statements providing information about # IUI p.

112. New York State Center of Excellence in Bioinformatics & Life Sciences R T U D-tuples 2.0: dealing with mistakes Validity and availability of information Tuple nameAttributesDescription D-tuple The particular referred to by IUI d registers the particular referred to by IUI A (the IUI for the corresponding A-tuple) at time t d. E is either the symbol ‘I’ (for insertion) or any of the error type symbols as defined in [1]. C is the reason for inserting the A-tuple. S is a list of IUIs denoting the tuples, if any, that replace the retired one. A D-tuple is inserted: (1)to resolve mistakes in RTS, and (2)whenever a new tuple other than a D-tuple is inserted in the RTS. [1] Ceusters W. Dealing with Mistakes in a Referent Tracking System. In: Hornsby KS (eds.) Proceedings of Ontology for the Intelligence Community 2007 (OIC-2007), Columbia MA, 28-29 November 2007;:5-8.OIC-2007

113. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Types of matches and mismatches

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

115. New York State Center of Excellence in Bioinformatics & Life Sciences R T U PtoP statements - particular to particular ordered sextuples of the form R i = IUI a is the IUI of the author of the statement, t a a reference to the time when the statement is made, r a reference to a relationship (available in o) obtaining between the particulars referred to in P, o a reference to the ontology from which r is taken, P an ordered list of IUIs referring to the particulars between which r obtains, and, t r a reference to the time at which the relationship obtains. P contains as much IUIs as required by the arity of r. In most cases, P will be an ordered pair such that r obtains between the particular represented by the first IUI and the one referred to by the second IUI. As with A statements, these statements must also be accompanied by a meta-statement capturing when the sextuple became available to the referent tracking system.

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

117. New York State Center of Excellence in Bioinformatics & Life Sciences R T U PtoN-statements N i = The person referred to by IUI a asserts at time t a that n i is the name of the nametype nt j that designates in the context IUI C in the real world the particular referred to by IUI p at t r. This template will further be referred to as PtoN template. Would assert that “Werner” is my first name, and “Ceusters” is my last name.

118. New York State Center of Excellence in Bioinformatics & Life Sciences R T U U - -tuples: “negative findings” Relation type Type of Negative Finding Examples % C1 * A particular is not related in a specific way to any instance of a universal at some given time he denies abdominal pain; no alcohol abuse; no hepatosplenomegaly; he has no children, without any cyanosis 85.4 C2 A particular is not the instance of a given class at some given time which ruled out primary hyperaldosteronism, nontender, in no apparent distress, Romberg sign was absent, no palpable lymph nodes 12.4 C3 A particular is not related to another particular in a specific way at some given time this record is not available to me; it is not the intense edema she had before; he has not identified any association with meals. 2.2 * ‘p’ ranges over particulars, ‘u’ over universals U  i = The particular referred to by IUI a asserts at time t a that the relation r of ontology o does not obtain at time t r between the particular referred to by IUI p and any of the instances of the universal u at time t r

119. New York State Center of Excellence in Bioinformatics & Life Sciences R T U PtoCO statements: particular to concept code Co i = IUI a is the IUI of the author of the statement, t a a reference to the time when the statement is made, cbs a reference to the concept-based system from which co is taken, IUI p the IUI referring to the particular which the author associates with co, co the concept-code in cbs which the author associates with p, and, t r a reference to the time at which the author considers the association appropriate,

120. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Interpretation of PtoCO statements must be interpreted as simple indexes to terms in a dictionary. All that such a statement tells us, is that within the linguistic and scientific community in which cbs is used, the terms associated with co may - i.e. are acceptable to - be used to denote p in their determinative version.

121. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A SNOMED-CT example #IUI-0945: author of the statement #IUI-1921: the left testicle of patient #IUI-78127 367720001: the SNOMED concept-code to which “left testis” is (in SNOMED) attached as term So we can denote #IUI-1921 by means of that left testis that entire left testis that testicle, that male gonad, that testis that genital structure that physical anatomical entity BUT NOT: that SNOMED-CT concept

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

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

124. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Data store

125. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Pragmatics of IUIs in EHRs IUI assignment requires an additional effort In principle no difference qua (or just a little bit more) effort compared to using directly codes from concept- based systems –A search for concept-codes is replaced by a search for the appropriate IUI using exactly the same mechanisms Browsing Code-finder software Auto-coding software (CLEF NLP software Andrea Setzer) –With that IUI comes a wealth of already registered information –If for the same patient different IUIs apply, the user must make the decision which one is the one under scrutiny, or whether it is again a new instance A transfert or reference mechanism makes the statements visible through the RTDB

126. New York State Center of Excellence in Bioinformatics & Life Sciences R T U < PtSession > < PtsInfo m_ PtL astName ="John" m_PtDOB ="01/01/1985 /> < PtVisitInfo m_PtTimeIn ="02/27/2007 02:44 PM"> … < Level1 m_TemplateName ="Fracture - femur" m_TemplateGUID="{13792543 - C66D - 4B47 - A055 - CEA1A0A53C87} > < Item m_Text=”Examination”> …. < Level4 m _TemplateName =” ” > < Item m_Text=" strength of left foot plantar flexion is 3/5; strength of left foot dorsi flexion is 2/5 ; " m_GUID="{65B26952 - 81A1 - 4291 - B26F - 344EBFD2B56B}" / > </ Level4 > …… </ Item > </ Level1 > < / PtVisitInfo > < / PtSessi on > MedtuityEMR Patient’s Encounter Document

127. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Decomposing EHR Statements into Particulars Information units in EHR statements are phrases. For each phrase, e.g. strength of left foot plantar flexion is 3/5, a list of templates containing references to defined classes and universals are stored in a database called Terms Configuration Database, describing the correct decomposition The decomposition of a phrase is based on our work described elsewhere *. *Rudnicki R., Ceusters W., Manzoor S and Smith B. What Particulars are Referred to in EHR Data? A Case Study in Integrating Referent Tracking into an Electronic Health Record Application. Accepted for American Medical Informatics Association 2007 Annual Symposium (AMIA 2007) Proceedings, Chicago IL, 10-14 November 2007.AMIA 2007 U1: The universal Person DC1: MMT scale data value 3. DC2: defined class whose members are a persons’ left foot plantar muscle group DC3: defined class whose members are the disposition of persons’ right foot plantar muscle groups to attain a certain performance on the heel-rise test DC4: defined class of persons who perform members of DC5 DC5: defined class whose members are acts of assessing the performance of heel-rise tests. DC6: defined class whose members are acts of left foot heel test carried out by a person. U2: clinical encounter

128. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Decomposing EHR Statements into Particulars Middleware component iterates through the XML document to retrieve the phrases. –For each phrase, e.g. strength of left foot plantar flexion is 3/5, middleware contacts with Terms Configuration Database to retreive the list of templates containing references to defined classes and universals. U1: The universal Person DC1: MMT scale data value 3. DC2: defined class whose members are a persons’ left foot plantar muscle group DC3: defined class whose members are the disposition of persons’ right foot plantar muscle groups to attain a certain performance on the heel-rise test DC4: defined class of persons who perform members of DC5 DC5: defined class whose members are acts of assessing the performance of heel-rise tests. DC6: defined class whose members are acts of left foot heel test carried out by a person. U2: clinical encounter

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

130. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 4: Applications & Projects

131. New York State Center of Excellence in Bioinformatics & Life Sciences R T U eyeGENE (June 2008 - …)

132. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology for Risks Against Patient Safety

133. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representing particular adverse event cases Is the generic representation of the portion of reality adequate enough for the description of particular cases? Example: a patient –born at time t 0 –undergoing anti-inflammatory treatment and physiotherapy since t 2 –for an arthrosis present since t 1 –develops a stomach ulcer at t 3. 133

134. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Anti-inflammatory treatment with ulcer development IUIDescription of particularProperties #1the patient who is treated#1 member_of 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_of C4 since t 2 #4#1’s arthrosis#4 member_of C5 since t 1 #5#1’s anti-inflammatory treatment#5 part_of #2 #5 member_of C2 since t 3 #6#1’s physiotherapy#6 part_of #2 #7#1’s stomach#7 member_of 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 (harm) 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 134

135. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Time line and dependencies (1) At t 0, the patient is born, and since that time, his stomach is part of him and a structure integrity (C8) inheres in it: –#1 instance-of person since t 0 –#7 part-of #1 since t 0 –#8 instance_of C8 since t 0 –#8 inheres_in #7 since t 0 #7’s structure integrity#8#1’s stomach#7 the patient (#1) who is treated #1 t0t0 t1t1 t2t2 t3t3 structure integrity C8 135

136. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Time line and dependencies (2) #7’s structure integrity#8#1’s stomach#7#1’s arthrosis#4 the patient who is treated #1 t0t0 t1t1 t2t2 t3t3 structure integrity C8 At t 1, the patient acquires arthrosis: –#4 member_of C5 since t 1 –#4 inheres_in #1 since t 1 underlying disease C5 136

137. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Time line and dependencies (3) #7’s structure integrity#8#1’s stomach#7#1’s arthrosis#4the physician responsible for #2#3#1’s physiotherapy#6#1’s anti-inflammatory treatment#5#1’s treatment#2 the patient who is treated #1 t0t0 t1t1 t2t2 t3t3 subject of care C1 involved structure C6 care giver C4 act of care C3 underlying disease C5 structure integrity C8 At t 2, the patient consults #3 who starts treatment. It is then that the patient becomes a member of the class subject of care (C1) and his stomach a member of the class involved structure (C6) 137

138. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Time line and dependencies … act under scrutiny C2 cognitive representation in #3 about #9#13noticing #9#12#1’s stomach ulcer#9#7’s structure integrity#8#1’s stomach#7#1’s arthrosis#4the physician responsible for #2#3#1’s physiotherapy#6#1’s anti-inflammatory treatment#5#1’s treatment#2 the patient who is treated #1 t0t0 t1t1 t2t2 t3t3 subject of care C1 involved structure C6 care giver C4 act of care C3 underlying disease C5 structure integrity C8 change brought about by #9#11 harmC10 138

139. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Domotics and RFID systems Avoiding adverse events in a hospital because of insufficient day/night illumination: –Light sensors and motion detectors in rooms and corridors and representations thereof in an Adverse Event Management System (AEMS) –What are ‘sufficient’ illumination levels for specific sites is expressed in defined classes, –Each change in a detector is registered in real time in the AEMS, –Action-logic implemented in a rule-base system, f.i. to generate alerts.

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

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

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

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

144. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Making existing EHR systems RT compatible

145. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Tracking versions of representations

146. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ways representational units do or do not refer OE: objective existence; ORV: objective relevance; BE: belief in existence; BRV: belief in relevance; Int.: intended encoding; Ref.: manner in which the expression refers; G: typology which results when the factor of external reality is ignored. E: number of errors when measured against the benchmark of reality. P/A: presence/absence of term.

147. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Revisioning beliefs

148. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Comparing terminologies with reality as benchmark

149. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Comparing ontology versions Ceusters W. Applying Evolutionary Terminology Auditing to the Gene Ontology. Journal of Biomedical Informatics 2009;42:518–529.

150. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Quality evolution of the Gene Ontology

151. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Quality forecasting

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

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

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

155. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some central ideas 1.Informative websites are about portions of reality. If the latter change, so should the former. 2.Synchronization should be auditable. 3.Enforce responsibility of information providers and consumers, yet protect their integrity. 4.Cross-fertilization with Information Artifact Ontology.

156. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some key insights Static versus dynamic pages; Web pages usually keep their name (URL), yet undergo changes; –‘page’ versus ‘file’ –Server file never ‘changes’: always replaced by a new file with the same name Changes to a file do not always involve changes to the propositional content; Requests to view a page do not lead the file on the server to be transmitted, but a new copy of it in each single case;

157. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Entities to assign IUIs to The content file of each page The content of each content file The propositional content of each content Each browser page Each checksum Each ontology and terminology used in RT-tuples Each RT-tuple (except D-tuples) The middleware component

158. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Use of the CEN Time Standard for HIT

159. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Tuple generations when adding a page

160. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Tuple generations when updating a page

161. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Tuple insertions: generating a browser page A-tuples nIUI p IUI a t ap Key 1#24#2(EVENT("#24 assignment") has-occ AT TP(time-18))#25 3#27#2(EVENT("#27 assignment") has-occ AT TP(time-20))#28 9#34#2(EVENT("#34 assignment") has-occ AT TP(time-26))#35 D-tuples nIUI d IUI A tdtd ECSKey 2#2#25(EVENT("#25 inserted") has-occ AT TP(time-19))ICE#26 4#2#28(EVENT("#28 inserted") has-occ AT TP(time-21))ICE#29 6#2#30(EVENT("#30 inserted") has-occ AT TP(time-23))ICE#31 8#2#32(EVENT("#32 inserted") has-occ AT TP(time-25))ICE#33 10#2#35(EVENT("#35 inserted") has-occ AT TP(time-27))ICE#36 12#2#37(EVENT("#37 inserted") has-occ AT TP(time-29))ICE#38 PtoP-tuples nIUI a tata rIUI o Ptrtr Key 5#2(EVENT("#30 is asserted") has-occ AT TP(time-22))MainContentCopyOf#022#27, #12(EPISODE("#30 is true") has-occ SINCE TI(time-20))#30 7#2(EVENT("#32 is asserted") has-occ AT TP(time-24))InstigatorOf#022#24, #27(EVENT ("#32 is true") has-occ AT TP(time-18))#32 11#2(EVENT("#37 is asserted") has-occ AT TP(time-28))ChecksumOf#022#34, #27(EPISODE("#37 is true") has-occ SINCE TI(time-26))#37