1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking: Research Topics and Applications Center for Cognitive Science, Buffalo NY Fall 2007 Colloquium – October 17, 2007 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 Imagine being in Saudi-Arabia, ready to open this window …

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U These are ‘First Order Entities’ This specific guy (#1) This specific ‘silver’ Audi (#2) This specific brick (#3)

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U But we are not here …

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U We are in something close to this … This specific image of #1 (#4) This specific image of #2 (#5) This specific image of #3 (#6) These are first order objects too …

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U … as well as second order #4 depicts #1 #6 depicts #3

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Not just first order is “in reality”, But so is second order

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Second order “in” first order An image of #9 (the hotel from which we would be looking to this car) (#10)

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Close your eyes … Try to imagine that specific ‘silver’ car … If everybody succeeded, there is #11, #12, #13, … i.e. a ‘mental image’ inside each of the attendees in this room

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A wealth of distinct, though closely related things The guy (of flesh and blood) standing next to that ‘silver’ car The images of that guy embodied in the projections on the screen a bit earlier The bits in the PP-file on this laptop that cause PP and the OS on this laptop and … to project an image of that guy on the screen The mental images of that guy in each of you, as well as the mental images of the images of that guy …

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The essence of Referent Tracking Keeping track of entities in reality By means of singular and globally unique identifiers (#1, #2, #3, …) That function as surrogates for these enities in information systems, documents, etc 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

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Thus: when WE use “#12” WE refer to THIS

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Unfortunately, it is not that easy … What is ‘THIS’ … –The car ? –The door ? –The reflected image of the hotel ? –That pixel on this image ? –… #12

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What is really needed … A ‘formal’ mechanism to –Assign numbers to entities –Specify :what the entities are –how they are related –Keep inventories of numbered entities –Let ‘systems’ know what number is assigned to an entity, and what entity is denoted by an assigned number

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Does mainstream Semantic Web thinking acknowledges this ? The answer is clearly: NO ! –Overemphasis on syntactic regimentation XML, RDF(s), OWL, … each have a semantics but users tend to assign more semantics to expressions therein than is actually carried. –Unwarranted trust in the power of description logics –Expressions in SW languages or ontologies are said to refer to ‘concepts’ –A complete mess in the terminology adhered to: Instance, object, resource, class, …

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Our proposal A combination of three theories/paradigms/... 1.Basic Formal Ontology what entities exist and how they are related 2.Granular Partition Theory how representations relate to reality 3.Referent Tracking how particular entities can be described unambiguously

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U BFO & GPT: a realist view of the world 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up through observations cognitive representations of the world; 3.To make these representations publicly accessible in some enduring fashion, these agents create representational artifacts that are fixed in some medium. Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A realist view of the world The world consists of –entities that are Either particulars or universals; Either occurrents or continuants; Either dependent or independent; and, –relationships between these entities of the form e.g. is-instance-of, e.g. is-member-of e.g. isa (is-subtype-of) Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A widespread misconception ‘A variety of clinical terminology standards exist in the health care system, giving meaning to raw data and allowing for semantic interoperability. Systemized Nomenclature of Medicine (SNOMED) clinical terms are one example of a clinical reference terminology that provides for semantic interoperability.’ Pharmacotherapy. 2005;25(8):1116-1125.

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 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. Using codes does not prevent ambiguities as to what is described: how many disorders are listed?

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Consequences Very difficult to: –Count the number of (numerically) different diseases Bad statistics on incidence, prevalence,... Bad basis for health cost containment –Relate (numerically the same or different) causal factors to disorders: –Dangerous public places (specific work floors, swimming pools), –dogs with rabies, –HIV contaminated blood from donors, –food from unhygienic source,... Hampers prevention –...

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Purpose: –explicit reference to the concrete individual entities relevant to the accurate description of each patient’s condition, therapies, outcomes,... Proposed Solution: Referent Tracking Now! That should clear up a few things around here ! Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Proposed Solution: Referent Tracking 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. Method: –Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity 78

24. 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 #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 inst-of at t 2

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

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Advantage: better reality 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 IUI-001 IUI-003 IUI-004 IUI-005 IUI-007 IUI-002 IUI-012 IUI-006 7 distinct disorders

27. 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.

28. 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.

29. 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.

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ultimate goal #IUI-1 ‘affects’ #IUI-2 #IUI-3 ‘affects’ #IUI-2 #IUI-1 ‘causes’ #IUI-3... Referent Tracking Database EHR CAG repeat Juvenile HD person disorder continuant Ontology

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ongoing research Prototype development Theory development –Dealing with ‘negative findings’ –Dealing with mistakes and change management in ontologies and repositories –Mapping databases –Unifying different views on reality (Psychiatry) Integration / interfacing with other systems –Medtuity, Praxis® EMR –Target Behavior Tracking (Sigmund Software) –Uniform Data Systems – patient outcome assessment –Change management in SNOMED-CT

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Dealing with negative findings Ceusters W, Elkin P, Smith B. Referent Tracking: The Problem of Negative Findings, Stud Health Technol Inform. 2006;124:741-6. Ceusters W, Elkin P, Smith B. Negative Findings in Electronic Health Records and Biomedical Ontologies: A Realist Approach. Forthcoming in International Journal of Medical Informatics

33. 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 2.The particular’s existence ‘may not already have been determined as the existence of something else’: 3.The particular may not have already been assigned a IUI. 4.It must be relevant to do so.

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘negative findings’: a challenge for RT Some examples: –“no history of diabetes” –“hypertension ruled out” –“absence of metastases in the lung” –“prevented abortion” –“cancelled X-Ray” RT does NOT allow a IUI to be assigned to what does not exist !

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Negative findings are important in care Occur relatively frequent: Elkin et al found SNOMED-CT to provide coverage for 14,792 concepts in 41 health records from Johns Hopkins University, of which 12.3% were identified as negative. Mutalik et al report the presence of 8,358 instances of UMLS concepts in 60 documents of which 6.8% were negative. Medico-legal issues: In 1998, an NHS Independent Review panel judged the record-keeping in a specific case to fall below the level of good practice because ‘the notes make no reference to any other findings, nor of any negative ones which would be relevant when considering problems specific to diabetes. Thus no reference is made to the absence of a smell of ketones on Miss J’s breath, nor any other negative indications’ In the US, Medicare and Medicaid compliance requires that the patient record should document ‘specific abnormal and relevant negative findings of the examination of the affected or symptomatic body area(s) or organ system(s)’

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Our strategy NOT to introduce in the referential machinery –Possibilia –Non-existent objects –Absences But to find the relationships that do obtain in reality between the entities involved

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Negative findings under a BFO perspective Relation type Type of Negative FindingExamples% 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

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Solution For C1 and C2: –Introduce a family of relations called ‘lacks’ such that for C1-type of negative findings (for example concerning ‘part’): –p lacks u at t with respect to part =def. there is no x such that: x part_of p at t and x instance_of u C2-type of negative findings: –p lacks u at t with respect to identity =def. there is no x such that: x identical_to p at t and x instance_of u –Introduce a new tuple-type in the RT-formalism 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 For C3: simple logical negation

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Testing the approach Our study sample: 396 negative findings encountered in 250 sentences out of 18 patient charts from Johns Hopkins University We excluded (8.3%) : –Misjudged negations: The patient actually answers yes, no, and sir to all questions’ –Negative formulation of positive phenomenon ‘He has no idea why he is here’ Her workup showed that she had an MRI of the brain that was negative in 03/02’ We ignored certain modalities: ‘He has no family history of GI malignancies that I know of’

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Results We were able to represent 99,9% of the negative findings using the lacks relation or logical negation of relationships between existing entities Failures only because of phenomena RT can’t (yet?) deal with: –‘no other complications of gastroesophageal reflux disease were noted’.

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusion With the introduction of the lacks family of relations, we have been able to –Provide additional support for the thesis that negation is outside the realm of ontology but belongs rather to the domains of logic, language and epistemology. –Stay away from ‘fantology’, i.e. the false belief that the structures of logic, language and information are mirrors of the structure of reality. In reality, there is only what there is. Language and logic allow us to talk and reason about what there is by using negation. But the corresponding negative expressions do not mirror anything in reality.

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Dealing with changes Ceusters W, Smith B. A Realism-Based Approach to the Evolution of Biomedical Ontologies. Proceedings of AMIA 2006, Washington DC, 2006;:121-125.AMIA 2006

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Change management 1.changes in the underlying reality (does the appearance or disappearance of an entry in a new version of an ontology or in an EHR relate to the appearance or disappearance of entities or of relationships among entities?); 2.changes in our scientific understanding; 3.reassessments of what is relevant for inclusion in an ontology or EHR; 4.encoding mistakes

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example: a person (in this room) ’s gender in the EHR In John Smith’s EHR: –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

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality versus belief, both in evolution t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 reference p4: an artifact on an image p5: a coin lesion that really corresponds with John’s tumor (IUI-#5): acknowledgement of the referential nature of p5

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 In the beginning, there was nothing …

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 Evolution (or some creative designer) brings benign tumors to existence, but we, poor humans, don’t know nothing yet…

48. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 The existence of benign tumors is acknowledged, but malignancy not yet …

49. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 We know about malignancy, a growth in John Doe, benign, came about, but we are not aware of it. Malignancy has been discovered.

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John consulted a physician, a picture is taken, it shows in reality a lesion, but it is not perceived.

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John’s tumor is being discovered, but that it turned malignant, remains unnoticed.

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 A second image is taken, that image shows a lesion that is correctly perceived, and allows to make the diagnosis of malignancy.

53. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John’s tumor is treated by means of RPCI’s irradation device, and wrongly believed to have disappeared.

54. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John is lucky: his tumor indeed disappeared. His physician is lucky: he escapes a maltreatment suite.

55. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 The government decides that malignancy doesn’t exist anymore: a convenient way to save on reimbursement and law suites.

56. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Why this story ? It shows the complex interrelationships between –What is the case; –What we know about what is the case; –What parts about what we know that is the case we wish to refer to in ontologies and repsoitories. These relationships can be used to design a metric for assessing improvements in the quality of –Electronic healthcare record data –Successive versions of ontologies –Mappings between ontologies.

57. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

58. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Valid presence Valid absence

59. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Unjustified presence Unjustified absence But sometimes you get lucky …

60. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Possible evolutions through versions

61. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Possible evolutions through versions An entity ceases to exist, but the representation is not updated:

62. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Chains of changes Belief at treality at t at t+1: unintended encoding corrected  If assumed that this is correct, then the unit at t must have been of type P-4, rather than P+1 a gain of +1 is assumed at t+2: the unit is assumed to refer to nothing, thus is removed from the representation  assumption at t+2 then at t+1 it was then at t it was

63. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Plans for future developments Making it scale –Unifying large data collections –The world in the machine Including its entire history Software agent development –Monitoring –Pattern recognition –Prediction