1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Improving Structured Electronic Health Record Data through Ontological Realism. July 27, 2011; 8.30 AM - 12.00 PM Marriott Buffalo Niagara, 1340 Millersport Highway ▪ Amherst, New York 14221 Werner CEUSTERS 1 and William R. HOGAN 2 1 Ontology Research Group, Center of Excellence in Bioinformatics and Life Sciences Department of Psychiatry, University at Buffalo, NY, USA 2 Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Agenda 8:30am: Introduction to Ontological Realism (WC) 9:00am: Problems with Mainstream EHR Designs (WC) 9:15am: Basic Formal Ontology and Referent Tracking as a Unifying Solution (WC) 9:45am: Representing Time in Referent Tracking (BH) 10:00am: Diagnoses and ICD-9-CM (BH) 10:30am: Break 10:45am: Keeping Track of Identifiers (BH) 11:15am: Adverse Events (WC) 11:45am: Data Warehousing for EHRs (WC & BH)

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Introduction to Ontological Realism Werner CEUSTERS Ontology Research Group, Center of Excellence in Bioinformatics and Life Sciences Department of Psychiatry, University at Buffalo, NY, USA

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

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; –by some philosophers taken to be synonymous with ‘metaphysics’ while others draw distinctions in many distinct ways (the distinctions being irrelevant for this talk), but almost agreeing on the following classification: metaphysics –general metaphysics »ontology –special metaphysics –distinct from ‘epistemology’ which is the study of how we can come to know about what exists.

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some MDs got it quite early… ‘We have to thank the metaphysicians for, if not explaining many things, at least giving us useful terms under which discussions may be carried on.’ Although: –‘Why should he, why should man, ever have been born, if to be born simply means to begin with his first breath a struggle against death?’ question in the realm of special metaphysics. Bryce PH. Ontology in relation to preventive medicine. Am J Public Health (N Y). 1912 Jan;2(1):32-3.

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U … and worse ‘If he thinks ontologically at all, he can no longer imagine human life only an accident, flotsam and jetsam on a turbulent and never-resting sea, but rather that "being" is an emanation from the Infinite Consciousness which makes the ego in us the occasion for the realization, by each - even in the smallest degree - that he is a constituent atom in Universal Mind, wherein is the definite idea illustrating not alone a common humanity, but also the manifestation of a universal good’. –here is not an ontological issue, but a psychiatric one. Bryce PH. Ontology in relation to preventive medicine. Am J Public Health (N Y). 1912 Jan;2(1):32-3.

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A legitimate ontological question Do mental illnesses / disorders / diseases exist? –The answer can, arguably, be ‘no’: if one does not subscribe to the mind-brain dichotomy: –no mind  nothing mental if one does, but also entertains a strong body-related interpretation of what is an illness, disorder, disease: –STEDMAN (27th edition): an interruption, cessation, or disorder of body function, system, or organ. –DORLAND: any deviation from or interruption of the normal structure or function of a part, organ, or system of the body as manifested by characteristic symptoms and signs; –WHO: an interconnected set of one or more dysfunctions in one or more body parts, linking to underling genetic factors and to interacting environmental factors and possibly: to a pattern or patterns of response to interventions. –and under the same conditions: ‘yes’: ‘mental disorder’ would be synonymous with ‘brain disorder’

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A legitimate ontological question Do mental illnesses / disorders / diseases exist? –The answer can, arguably, be ‘no’: if one does not subscribe to the mind-brain dichotomy: –no mind  nothing mental if one does, but also entertains a strong body-related interpretation of what is an illness, disorder, disease: –STEDMAN (27th edition): an interruption, cessation, or disorder of body function, system, or organ. –DORLAND: any deviation from or interruption of the normal structure or function of a part, organ, or system of the body as manifested by characteristic symptoms and signs; –WHO: an interconnected set of one or more dysfunctions in one or more body parts, linking to underling genetic factors and to interacting environmental factors and possibly: to a pattern or patterns of response to interventions. –and under the same conditions: ‘yes’: ‘mental disorder’ would be synonymous with ‘brain disorder’

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A better phrased ontological question Do mental illnesses / disorders / diseases exist? What, if anything at all, do the terms ‘mental illness’, ‘mental disorder’, … denote?

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A better phrased ontological question Do mental illnesses / disorders / diseases exist? What, if anything at all, do the terms ‘mental illness’, ‘mental disorder’, … denote? termsfirst-order reality ‘mental disorder’ ‘person’ ‘UB’

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U This is distinct from terminological questions Terminological question: –What does ‘mental disorder’ mean ?

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U This is distinct from terminological questions Terminological question: –What does ‘mental disorder’ mean ? ‘mental disorder’ meaning of ‘mental disorder’

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminological approaches raise further questions Terminological question: –What does ‘mental disorder’ mean ? ‘mental disorder’ meaning of ‘mental disorder’ What are meanings? Do meanings denote? How are these related?

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The half-baked semantic/semiotic triangle does not provide any good answers despite its overwhelming popularity ‘mental disorder’ meaning of ‘mental disorder’ Concept Symbol / Sign / TermThing / Referent

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The semantic triangle works sometimes fine term concept referent ‘Beethoven’ Ludwig van Beethoven that great German composer that became deaf …

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The semantic triangle works sometimes fine term concept referent ‘Beethoven's Symphony No. 3’ Beethoven’s symphony dedicated to Bonaparte the symphony played after the Munich Olympics massacre … ‘Beethoven's Opus 55’ ‘Eroica’

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Sometimes the semantic triangle fails term concept referent ‘Beethoven's Symphony No. 11’ the symphony Beethoven wrote after the tenth …

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Sometimes the semantic triangle fails term concept referent ‘Beethoven's Symphony No. 11’ the symphony Beethoven wrote after the tenth … some hold this term has meaning

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Sometimes the semantic triangle fails term concept referent ‘Beethoven's Symphony No. 10’ the one assembled by Barry Cooper from fragmentary sketches Beethoven’s hypothetical symphony …

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Prehistoric ‘psychiatry’: drapetomania term concept referent ‘drapetomania’ disease which causes slaves to suffer from an unexplainable propensity to run away … painting by Eastman Johnson. A Ride for Liberty: The Fugitive Slaves. 1860.

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some etiologic and diagnostic reflections

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The North’s ‘Effugium Discipulorum’

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The questions the triangle raises become trickier Is … –Beethoven’s 10 th symphony a symphony ? –Beethoven’s 10 th symphony a hypothetical symphony ? –a hypothetical symphony a symphony ? In medicine, is … –a prevented abortion an abortion ? –an absent nipple a nipple ?

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology should give the answer In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other;

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Unfortunately, ‘ontology’ denotes ambiguously In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; In computer science and many biomedical informatics applications: –An ontology (plural: ontologies) is a shared and agreed upon conceptualization of a domain;

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Semantic Applications use Ontologies and Software 27 Domain ‘Philosophical’ approach to ontology Ontologies Ontology Authoring Tools Reasoners create Computer Science approach to ‘ontology’

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology in CS: relates concepts drapetomania slavemental disorderrunning awaypropensity How concepts are / can be related

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Three, thus far unrelated, ways of relating drapetomania slavemental disorderrunning awaypropensity How concepts are / can be related How referents are related How terms are related

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology as it should be done In philosophy: –Ontology (no plural) is the study of what entities exist and how they relate to each other; In computer science and many biomedical informatics applications: –An ontology (plural: ontologies) is a shared and agreed upon conceptualization of a domain; The realist view within the Ontology Research Group combines the two: –We use Ontological Realism, a specific methodology that uses ontology as the basis for building high quality ontologies, using reality as benchmark.

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 1.There is an external reality which is ‘objectively’ the way it is; 2.That reality is accessible to us; 3.We build in our brains cognitive representations of reality; 4.We communicate with others about what is there, and what we believe there is there. The basis of Ontological Realism Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontological Realism makes three crucial distinctions 1.Between data and what data are about; 2.Between continuants and occurrents; 3.Between what is generic and what is specific. 32 Smith B, Ceusters W. Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies. Applied Ontology, 2010.

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 33

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U L1 - L2 L3 34 Linguistic representations about (1), (2) or (3) Clinicians’ beliefs about (1) Entities (particular or generic) with objective existence which are not about anything Representations First Order Reality

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U observation & measurement A crucial distinction: data and what they are about data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome First- Order Reality Representation is about 35

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontological Realism makes crucial distinctions Between data and what data are about; Between continuants and occurrents: –obvious differences: a person versus his life a disease versus its course space versus time –more subtle differences: observation (data-element) versus observing diagnosis versus making a diagnosis message versus transmitting a message 36

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Is depression considered a continuant or occurrent? What do we mean by ‘depression’ ? –The name of some disease ?  continuant –A bout of feelings of being worth nothing, sobbing, appearance of suicidal thoughts, …  occurrent

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Between what is generic and what is specific. L1. First-order reality L2. Beliefs (knowledge) GenericSpecific DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MOLECULE PERSON DISEASE PATHOLOGICAL STRUCTURE PORTION OF INSULIN DRUG me my blood glucose level my NIDDM my doctor my doctor’s computer L3. Representation ‘person’‘drug’‘insulin’‘W. Ceusters’‘my sugar’ GenericSpecific 38

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U tt t instanceOf The essential pieces material entity spacetime region me some temporal region my life my 4D STR some spatial region history spatial region temporal region dependent continuant some quality located-in at t … at t participantOf at toccupies projectsOn projectsOn at t

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Problems with mainstream EHR designs Werner CEUSTERS Ontology Research Group, Center of Excellence in Bioinformatics and Life Sciences Department of Psychiatry, University at Buffalo, NY, USA

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Benefits of Electronic Health Records (EHRs) for providers and their patients: –Complete and accurate information, shared, coordinated, –Better access to information, when and where needed, –Patient empowerment, proactive, consent. ONCHIT: http://healthit.hhs.gov

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U ONCHIT’s Legislation and Regulations The Health Information Technology for Economic and Clinical Health (HITECH) Act allows HHS to promote health information technology (HIT) to improve health care quality, safety, and efficiency. Results : –Incentive Program for EHRs issued by CMS: Stage I requirements for certified EHR technology in order to qualify for the payments: ‘Meaningful Use’ – 2011-2012; –Standards and Certification Criteria for EHRs; –Request for Comment - Stage 2 Definition of Meaningful Use in 2013 - 2014. ONCHIT: http://healthit.hhs.gov

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Examples of imposed standards Patient summary record: –HL7 CDA R2 (CCD) or ASTM E2369 (CCR). Problem list: –ICD-9-CM or SNOMED CT®. Procedures: –ICD-9-CM or HCPCS + CPT-4. Laboratory orders and results: –LOINC®.

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some fallacies 1.Crippled idea about ‘problem list of diagnoses’

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Crippled idea about ‘problem list of diagnoses’ Basis of Problem List: –Larry Weed’s Problem Oriented Medical Record Each medical record should have a complete list of all the patient's problems, including both clearly established diagnoses and all other unexplained findings that are not yet clear manifestations of a specific diagnosis. Includes: –diagnosis− physical finding –lab abnormality− physiologic finding –social issue− symptom –demographic issue Weed LL. Medical records that guide and teach. N Engl J Med. 1968 Mar 14;278(11):593-600.

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some fallacies 1.Crippled idea about ‘problem list of diagnoses’ 2.Conflation of diagnosis and disease/disorder

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conflation of diagnosis and disease/disorder The disorder is thereThe diagnosis is here The disease is there

48. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some fallacies 1.Crippled idea about ‘problem list of diagnoses’ 2.Conflation of diagnosis and disease/disorder 3.The structure of EHR data (information model) is not close enough to the structure of that what the data are about

49. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U EHR Information Models (simplified) patient diagnosis drug finding encounterpatient diagnosis drug finding

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some fallacies 1.Crippled idea about ‘problem list of diagnoses’ 2.Conflation of diagnosis and disease/disorder 3.The structure of EHR data (information model) is not close enough to the structure of that what the data are about 4.Unjustified belief that the use of unambiguous codes renders EHR data unambiguous

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Using generic representations for specific entities is inadequate 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 PtIDDateSNOMED CT codeNarrative 093920/12/1998255087006malignant polyp of biliary tract

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some fallacies 1.Crippled idea about ‘problem list of diagnoses’ 2.Conflation of diagnosis and disease/disorder 3.The structure of EHR data (information model) is not close enough to the structure of that what the data are about 4.Unjustified belief that the use of unambiguous codes renders EHR data unambiguous 5.Popular ontologies will solve the problems

53. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Un-‘realistic’ SNOMED hierarchy ‘Fractured nasal bones (disorder)’ –is_a ‘bone finding’ synonym: ‘bone observation’ Confusion between L3. L3. ‘fractured nose’ [appearing in some record]: the expression of an observation) L2. ‘ fractured nose ’ [in someone’s mind]: content of an act of observation L1. fractured nose: a type of nose, a particular nose

54. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The visible results of Kantianism and OWL-ism 54

55. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 55

56. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 56

57. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U MedDRA: violations of all terminological rules 57

58. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mistakes in the NCI Thesaurus OWL version 58 Schulz S, Schober S, Tudose I, Stenzhorn H: The Pitfalls of Thesaurus Ontologization – the Case of the NCI Thesaurus. AMIA Annu Symp Proc, 2010: 727- 731 (AMIA 2010 Annual Symposium, Washington D.C. USA, November 2010): http://proceedings.amia.org/127gtf/1http://proceedings.amia.org/127gtf/1

59. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Mistakes in the NCI Thesaurus OWL version The NCIT ignores the relationships between representation and reality: –Functions as subclasses of processes: the bearer of a function is not necessarily participant of a process. –Domain incompatibilities: interpreting relation names as containing domain constraints (without being backed-up by any logical definition). –Individuals expressed as classes: like in Nicaragua subClassOf Conceptual_Part_Of some North_America. 59 Schulz S, Schober S, Tudose I, Stenzhorn H: The Pitfalls of Thesaurus Ontologization – the Case of the NCI Thesaurus. AMIA Annu Symp Proc, 2010: 727- 731 (AMIA 2010 Annual Symposium, Washington D.C. USA, November 2010): http://proceedings.amia.org/127gtf/1http://proceedings.amia.org/127gtf/1

60. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A problem of education Consider the wine regions. Initially, we may define main wine regions, such as France, United States, Germany, and so on, as classes and specific wine regions within these large regions as instances. For example, Bourgogne region is an instance of the French region class. However, we would also like to say that the Cotes d’Or region is a Bourgogne region. Therefore, Bourgogne region must be a class (in order to have subclasses or instances). However, making Bourgogne region a class and Cotes d’Or region an instance of Bourgogne region seems arbitrary: it is very hard to clearly distinguish which regions are classes and which are instances. Therefore, we define all wine regions as classes. 60 Ontology Development 101: A Guide to Creating Your First Ontology Natalya F. Noy and Deborah L. McGuinness

61. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Basic Formal Ontology and Referent Tracking as a unifying solution Werner CEUSTERS Ontology Research Group, Center of Excellence in Bioinformatics and Life Sciences Department of Psychiatry, University at Buffalo, NY, USA

62. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The ultimate goal of Healthcare IT Everything collected wherever, whenever and about whomever which is relevant to a medical problem in whomever, whenever and wherever, should be accessible without loss of relevant detail.

63. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U If it is possible outside healthcare … received confirmation call Note in ‘EHR’ about calories purchased (or card blocked?)

64. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U This raises many questions Is this … - possible ? - desirable ? - scary ?

65. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U This raises many questions Is this … - possible ? I don’t care too much about these

66. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A non-trivial relation ReferentReference 66

67. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A non-trivial relation ReferentReference Concept ? 67

68. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some key questions What referents, if any at all, are depicted by a putative reference? How do changes at the level of the referents correspond with changes in the collection of references? If references are transmitted, how can the receiver know what referents are depicted? ReferentReference 68

69. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 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 –…

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

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

72. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Between what is generic and what is specific. L1. First-order reality L2. Beliefs (knowledge) GenericSpecific DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MOLECULE PERSON DISEASE PATHOLOGICAL STRUCTURE PORTION OF INSULIN DRUG me my blood glucose level my NIDDM my doctor my doctor’s computer L3. Representation ‘person’‘drug’‘insulin’‘W. Ceusters’‘my sugar’ Referent TrackingBasic Formal Ontology GenericSpecific 72

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

74. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The problem of reference in free text ‘The surgeon examined Maria. She found a small tumor on the left side of her liver. She had it removed three weeks later.’ Ambiguities: –who denotes the first ‘she’: the surgeon or Maria ? –on whose liver was the tumor found ? –who denotes the second ‘she’: the surgeon or Maria ? –what was removed: the tumor or the liver ? Here referent tracking can come to aid.

75. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Use these identifiers in expressions using a language that acknowledges the structure of reality: e.g.: a yellow ball: then not : yellow(#1) and ball(#1) rather: #1: the ball#2: #1’s yellow Then still not: ball(#1) and yellow(#2) and hascolor(#1, #2) but rather: instance-of(#1, ball, since t1) instance-of(#2, yellow, since t2) inheres-in(#1, #2, since t2) Fundamental goals of ‘our’ Referent Tracking  Strong foundations in realism-based ontology

76. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 76 Unconstrained reasoning OWL-DL reasoning Sorts of relations U1U2 P1 P2 UtoU: isa, partOf, … PtoU: instanceOf, lacks, denotes… PtoP: partOf, denotes, subclassOf,…

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

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

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

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

81. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The 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 … … time stamp in case of continuants

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

83. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Big Picture

84. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U a disease is a disposition rooted in a physical disorder in the organism and realized in pathological processes. etiological processdisorderdispositionpathological process abnormal bodily featuressigns & symptomsinterpretive processdiagnosis producesbearsrealized_in producesparticipates_inrecognized_as produces Basis of OGMS

85. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Disease course the totality of all P ROCESSES through which a given D ISEASE instance is realized. multiple D ISEASE C OURSES will be associated with the same D ISORDER type, for example in reflection of the presence or absence of pharmaceutical or other interventions, of differences in environmental influence, and so forth.

86. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Clinical Picture =def. – A representation of a clinical phenotype that is inferred from the combination of laboratory, image and clinical findings about a given patient. Diagnosis =def. – –A conclusion of an interpretive process that has as input a clinical picture of a given patient and as output an assertion to the effect that the patient has a disease of such and such a type. Diagnosis

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

88. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representing time in Referent Tracking William R. HOGAN, MD, MS Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA

89. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Time and Referent Tracking Assign instance unique identifiers (IUIs) to temporal entities Use these IUIs for the temporal parameters of templates: –t ap –t a –t r –t d

90. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Problem: Limitations of Current Handling of Time in BFO/RO For example, we want to say: –W. Hogan participates_in W. Hogan’s life at v, where v is a temporal interval –W. Hogan’s life instance_of Process Issues: –BFO/RO are insufficiently developed to allow such statements over temporal intervals –No mechanism exists in BFO/RO/RTS (or elsewhere) to handle open-ended intervals –RT templates require t r parameter in all cases, including relations among occurrents

91. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Expanding bfo and ro to temporal intervals

92. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Basic Formal Ontology

93. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relating Processes and Time * p occurring_at t = [definition] for some c, p has_participant c at t t first_instant p = [definition] p occurring_at t and for all t1, if t1 earlier t, then not p occurring_at t1 t last_instant p = [definition] p occurring_at t and for all t1, if t earlier t1, then not p occurring_at t1 Limitation: each relation here connects processes and only temporal instants. * Smith B, et al. Relations in Biomedical Ontologies. Genome Biology 2005, 6:R46 (doi:10.1186/gb-2005-6-5-r46).

94. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Expanding to Intervals Preliminary –Recall: t earlier t1 (two temporal instants) –t starts v: informally, t is first instant of interval * –t ends v: informally, t is last instant of interval * Then: –t during v = [definition] t1 starts v, t2 ends v, and (t1 earlier t and t earlier t2) or (t = t1) or (t = t2) –p occuring_at v = [definition] for all t during v, p occurring_at t *as defined by Trentleman et al. An axiomatization of Basic Formal Ontology with projection functions. Proceedings of the 6 th Australasian Ontology Workshop. 2010. Available at: http://krr.meraka.org.za/~aow2010/Trentelman-etal.pdfhttp://krr.meraka.org.za/~aow2010/Trentelman-etal.pdf

95. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Similarly, for BFO c instance_of C at v = [definition] for all t during v, c instance_of C at t c part-of c 1 at v = [definition] for all t during v, c part_of c 1 at t Etc…

96. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Temporal relations that handle points and intervals

97. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Temporal Relations We use an expanded set of temporal relations that begin with Allen * Allen’s relations are for intervals with definite end points Therefore, we must expand to include: –Instants –Intervals with unknown endpoint * Allen JF. Maintaining knowledge about temporal intervals. Communications of the ACM. 1983, 26(11):832-43.

98. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relations Between Intervals with Known Boundaries Ceusters W, et al. TSMI: a CEN/TC251 standard for time specific problems in healthcare informatics and telematics. Int J Med Inform. 1997, 46:87-101.

99. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ceusters W, et al. TSMI: a CEN/TC251 standard for time specific problems in healthcare informatics and telematics. Int J Med Inform. 1997, 46:87-101. Relations Between Intervals with Known Boundaries

100. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relations that can handle open-ended intervals

101. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Possible Relations Between Two Intervals with Unknown Right Boundary Interval of W. Hogan’s life: Interval of J. Page’s life: Since Co-ends During

102. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Enhanced Temporal Relation Ontology

103. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Back to referent tracking issues

104. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Handling Relations Among Occurrents Problem: –PtoP, PtoU, PtoLackU, and PtoCO templates require t r regardless of whether they involve continuants or occurrents –But, relations among occurrents are not time indexed* Solution: assign an IUI to the maximal temporal interval –Left boundary is beginning of time –Right boundary is open ended * Smith B, et al. Relations in Biomedical Ontologies. Genome Biology 2005, 6:R46 (doi:10.1186/gb-2005-6-5-r46).

105. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Avoiding Infinite Regress A If we assign an IUI to a temporal instant: –A –…–… To break the recursion: A That is, we assign an IUI to the instant at itself.

106. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example: Representing Birthdate

107. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Assigning IUIs to Relevant Entities EntityAssigning IUI to Entity W. Hogan A W. Hogan’s birth A Instant of W. Hogan’s birth A Day containing birth instant A Time of IUI assignment A Time of template assertion A Maximal temporal interval A

108. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relations among Entities W. Hogan is the agent of his birth at instant of birth: PtoP, iui bi > W. Hogan’s birth occurs at the instant of birth: PtoP, iui always > The instant of birth is during birth date: PtoP, iui always > The birth date has a name according to the Gregorian calendar system: PtoN

109. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Summary Approach is to represent temporal entities just as other entities: assign IUI Requires extensions to BFO and RO to handle: –Intervals –Relations among intervals –Open-ended intervals and their relations Requires extensions to Referent Tracking –IUI of maximum time interval (i.e., “always”) –Breaking recursion by assigning IUI to temporal instant at that instant

110. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Diagnoses and ICD-9-CM William R. HOGAN, MD, MS Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA

111. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Motivation In the ideal future state, where data are recorded using good ontologies, what do we do with bad legacy data? How do we integrate good and bad data during the transition? –Basic science (genomics, proteomics, etc.) For example, Gene Ontology –Administrative “disease” data (billing) International Classification of Diseases (ICD)

112. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U ICD-9-CM 9 th Revision of ICD, U.S. Clinical Modification Used in U.S. for billing since 1983 (Medicare Part A) and 1994 (Medicare Part B) Huge quantities of data exist in U.S. encoded with ICD-9-CM (most of it insurance claims) ICD-9-CM coded data are generally bad * * O'Malley KJ, et al. Measuring diagnoses: ICD code accuracy. Health Serv Res. 2005 Oct;40(5 Pt 2):1620-39.

113. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Disease != Diagnosis Not just because ontologists say so Stedman’s Medical Dictionary, 24 th ed (1982) –Disease: Morbus; illness; sickness; an interruption, cessation, or disorder of body functions, systems, or organs. –Diagnosis: The determination of the nature of a disease.

114. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U ICD-9-CM Codes as Statements …Or more precisely, statement templates, or types of statements Why? Coding guidelines refer to diagnosis codes Combination codes Epistemology Not otherwise specified

115. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U ICD-9-CM Codes Are Diagnosis Codes Because the Official Coding Guidelines say so “Combination codes” –Per Guidelines, used to …classify two diagnoses… –One code indicates presence of multiple diseases, not one disease of multiple types –Hypertensive heart and chronic kidney disease, benign, with heart failure and with chronic kidney disease stage I through stage IV, or unspecified

116. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Not Otherwise Specified “specified” is not an attribute that diseases have Here, it is the passive form of the verb to specify Being the direct object of a verb does not of necessity alter your fundamental nature That is, the disease does not change because a human specifies it in a certain way (or not) Thus, it is the medical record that specifies, even per the Coding Guidelines Or presumes it, or knows it, or un-knows it, or finds it, or diagnoses it, or observes it, or finds it on exam, or finds it on microscopy, or finds it by CT scan, or ….

117. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Assignment of ICD-9-CM Code as Clinical Statement The assignment of an ICD-9-CM code to a particular patient at a particular time –Diagnostic statement –More generally, since not all codes are about diseases, a clinical statement Example: Dr. Jones, 07/10/2010, Jane Doe, ICD-9-CM, 482.31, Group A streptococcal pneumonia, 07/07/2010

118. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Goal Represent the entities and their relationships implied by the diagnostic statement –Disease –Disease course –Disorder –Pathological process –Anatomical entity

119. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Realist Approach Definition of disease, disorder, etc. –Scheuerman et al –Ontology for General Medical Science As much as possible, use relations and types from other ontologies –Relation Ontology (RO) –Foundational Model of Anatomy (FMA) –Disease Ontology (DO) –Chemical Entities of Biological Interest (ChEBI) –Ontology for Biomedical Investigations (OBI)

120. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U OGMS Definitions Disease: A disposition (i) to undergo pathological processes that (ii) exists in an organism because of one or more disorders in that organism. Diagnosis: The representation of a conclusion of an interpretive process that has as input a clinical picture of a given patient and as output an assertion (diagnostic statement) to the effect that the patient has a disease of such and such a type.

121. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U OGMS Definitions (continued) Disorder: A material entity which is clinically abnormal and part of an extended organism. Pathological bodily process: a bodily process that is clinically abnormal.

122. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U PARTICULARS From here on out, this talk is fundamentally about particulars –Relations between pairs of particulars –The types they instantiate (the only mention of types) This work does NOT seek to define types or universals

123. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Why Not Mappings at Type Level? For ICD-9-CM, at least, it is not one-to-one: –Primary tuberculous infection, tubercle bacilli not found (in sputum) by microscopy, but found by bacterial culture –Hypertensive heart and chronic kidney disease, malignant, without heart failure and with chronic kidney disease stage I through stage IV, or unspecified –Railway accident involving derailment without antecedent collision, injuring pedal cyclist These are statements, which do not go in an ontology

124. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representing Disease/Disorder At a minimum, for any given ICD about a disease, assigned to a person, hs, as holding at t r : hs instance_of Human at t r dz instance_of Disease at t r do instance_of Disorder at t r dz disposition_of do at t r dz inheres_in hs at t r Conventions: particulars:italics, lower case universals:italics, First letter upper case relations:bold For clarity’s sake, times are implied from now on. 124

125. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U ICD-9-CM Codes that Imply HTN 51 codes * Why so many? Combination codes. –Hypertension –Benign vs. malignant –Heart disease –Chronic kidney disease (CKD) –CKD stages –Heart failure * Available at: http://spreadsheets0.google.com/ccc?key=t_Zr0cEjaxoCYwp11ahg84g&hl=en#gid=0http://spreadsheets0.google.com/ccc?key=t_Zr0cEjaxoCYwp11ahg84g&hl=en#gid=0

126. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U CodeTitle 401.0Essential hypertension, malignant 401.1Essential hypertension, benign 402.00Hypertensive heart disease, malignant, without heart failure 402.01Hypertensive heart disease, malignant, with heart failure 403.10Hypertensive chronic kidney disease, benign, with chronic kidney disease stage I through stage IV, or unspecified 403.11Hypertensive chronic kidney disease, benign, with chronic kidney disease stage V or end stage renal disease 404.00Hypertensive heart and chronic kidney disease, malignant, without heart failure and with chronic kidney disease stage I through stage IV, or unspecified 404.01Hypertensive heart and chronic kidney disease, malignant, with heart failure and with chronic kidney disease stage I through stage IV, or unspecified

127. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Hypertension Additionally: dz instance_of Hypertension Under the renal-salt-reabsorption theory: do instance_of Scattered molecular aggregate mo instance_of NaCl molecule do has_grain mo pb instance_of Portion of blood do part_of pb pb part_of hs

128. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Implementation in Referent Tracking Formalism for making statements about particulars –Implemented by Ceusters/Manzoor as Semantic Web application –RDF/RDFS based Each particular is assigned an Instance Unique Identifier (IUI) Templates, one for each type of statement about particulars

129. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Templates * –A: assign instance unique identifier (IUI) to particular –PtoU: particular instantiates universal –PtoP: relation b/w two particulars –PtoLackU: particular lacks relation R to any particular that instantiates U *This is a partial list of RT templates

130. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Examples All particulars mentioned: do, dz, hs, pb, mo, etc. require an A template: A PtoU template PtoU PtoP template PtoP, iui tr >

131. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A Note on Time We rarely know when the disease and disorder began to exist For hypertension, we additionally do not know the right boundary of the interval We can say, however, if the diagnosis is dated, that the time of diagnosis is during disease interval: PtoP, iui always >

132. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Presence vs. Absence of Heart Failure Presence –dz_2 instance_of Heart failure –do_2 instance_of Disorder –ht instance_of Heart –dz_2 disposition_of do_2 –do_2 part_of ht –ht part_of hs Absence –hs lacks Heart failure with respect to bearer_of

133. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U PtoLackU Example PtoU - The person denoted by iui hs has no bearer_of relationship to any instance of Heart failure

134. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusions The assignment of ICD-9-CM code to particular patient is a clinical statement We can represent the disease/disorder/etc. particulars implied by these statements Hypertension fits nicely into the OGMS framework The Referent Tracking paradigm is well-suited to operationalizing the method

135. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Future Work More of ICD-9-CM ICD-10-CM SNOMED CT 135

136. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Keeping track of identifiers William R. HOGAN, MD, MS Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA

137. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Motivation Interoperability with other systems that use identifiers to denote particulars: –Person, encounter (EHRs) –U.S. States: AK, AL, AR, …, WA –Organizations –Even instances of software systems (e.g., installation of EHR at ABC Clinic) A.K.A. Local Identifiers

138. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U How to Include Local Identifiers in a Referent Tracking System (RTS)? Suppose EHR at ABC Clinic uses the identifier “P000123” to refer to Mrs. Smith Then: –Where in the templates does “P000123” go? –How do we connect it to IUI for Mrs. Smith? –How do we say that the identifier “P000123” came from ABC Clinic’s EHR? –How do we handle all the time parameters of the needed templates?

139. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Basic Answer Assign the identifier an IUI: A

140. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Now You Can Say Things About It Mrs. Smith’s identifier is an instance of a centrally registered identifier PtoU It is part of the person id system PtoP, iui tr > And perhaps most importantly, it denotes Mrs. Smith! PtoP, iui tr >

141. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U But Where Does “P000123” Go? In a PtoN template General form: PtoN Where: –nt = name type –n = name –iui p = entity named –iui c = user of name to refer to iuip For “P000123”: PtoN

142. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Similarly, for the Identifier System The person id system is an instance of a centrally registered identifier registry PtoU It is part of the EHR system PtoP, iui tr > It has a name PtoN

143. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Good, But Not Ideal Name type parameter overloaded – requires ontology of types of names Leaves some relations implicit –Endorsement of identifiers by organizations, people, etc. Better solution will be to develop theory of denotational bonds as first introduced by Ceusters at ICBO 2009

144. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Acknowledgements The OGMS Team Smith, Ceusters, Scheuermann, Goldfain, Arabandi, James, Ogbuji, Merico, Cowell, Ruttenburg, et al. The Referent Tracking Team Ceusters, Manzoor, et al. Richard Lifton, MD, PhD Award numbers 1UL1RR029884 and 3 P20 RR016460-08S1 from the National Center for Research Resources The content is solely the responsibility of the author and does not necessarily represent the official views of the National Center for Research Resources or the National Institutes of Health. 144

145. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Adverse events 145 Werner CEUSTERS Ontology Research Group, Center of Excellence in Bioinformatics and Life Sciences Department of Psychiatry, University at Buffalo, NY, USA

146. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 146 ReMINE Project Ceusters W, Capolupo M, Smith B, De Moor G. An Evolutionary Approach to the Representation of Adverse Events. In: Medical Informatics Europe 2009, Sarajevo, Bosnia and Herzegovina, August 31, 2009. Studies in health technology and informatics 150;:537-541.

147. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 147 Ontology development in ReMINE ReMINE Taxonomies ReMINE Adverse Event Domain Ontology ReMINE Application Ontology ReMINE Application Ontology ReMINE Application Ontology ReMINE Application Ontologies ReMINE Taxonomies ReMINE Taxonomies ReMINE Taxonomies Description of specific adverse event domains (childbirth, patient transfer,..) as cognized by human beings Realism-based, purpose independent representation of the portion of reality described in the taxonomies Purpose dependent reformulations of the parts of RAEDO which are relevant for a specific domain support annotation support reasoning higher order logic description logic

148. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 148 Risk Manager’s Event Administration System ReMINE Taxonomy Annotated Events

149. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 149 ReMINE’s notion of adverse event 1.an ‘incident [that] occurred during the past and [is] documented in a database of adverse events’ –Stefano Arici, Paolo Bertele. ReMINE Deliverable D4.1 – RAPS Taxonomy: approach and definition. V1.0 (Final) August 8, 2008. (p21) … which is a ‘perdurant’ - ibidem (p26) … ‘that occurs to a patient’ - ibidem (p23) 2.an expectation of some future happening that can be prevented - ibidem (p23)

150. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 150 Terminologists agree, ontologists think … Can something which is an incident be at the same time an expectation ? Can something which is an incident a time t, later become an adverse event simply because it [?] has been entered in a database ? Can adverse events really occur in software ? …

151. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 151 Intermediate conclusion The ReMINE taxonomy (and all concept-based terminologies and ‘ontologies’ in general) provides a distorted view of reality. For good reasons: the distortion is such that –it reflects a pragmatic view on what is relevant for the purposes it is designed, –it does away with complexities that do not help human beings in doing a better job. But with some negative consequences: –reusability out of the ReMINE context is hampered, –integration with other descriptive systems becomes cumbersome, and –advanced reasoning turns out to be impossible.

152. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 152 Using the 3 levels and the particular/universal/class distinctions Level 1: –#1: an incident that happened in the past; Level 2: –#2: the interpretation by some cognitive agent that #1 is an adverse event; –#3: the expectation by some cognitive agent that similar incidents might happen in the future; Level 3: –#4: an entry in the adverse event database concerning #1; –#5: an entry in some other system about #3 for mitigation or prevention purposes.

153. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 153 Allows appropriate error management Some possibilities: 1.#1with unjustified absence of #2: #1 was not perceived at all, or not assessed as being an adverse event 2.Unjustified presence of #2: There was no #1 at all, or #1 was not an adverse event 3.Unjustified absence of #4 Same reasons as under (1) above Justified presence of #2 but not reported in the database –… Ceusters W, Smith B. A Realism-Based Approach to the Evolution of Biomedical Ontologies. Proceedings of AMIA 2006, Washington DC, 2006;:121-125.

154. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 154 Part of the ReMINE Domain Ontology

155. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 155 Higher order logical representation an incident (#1) that happened at time t2 to a patient (#2) after some intervention (#3 at t1) is judged at t3 to be an adverse event, thereby giving rise to a belief (#4) about #1 on the part of some person (#5, a caregiver as of time t6). This requires the introduction (at t4) of an entry (#6) in the adverse event database (#7, installed at t0).

156. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U 156 Advantages Synchronisation of two distinct representations of the same reality: –taxonomies: user-oriented view data annotation Domain ontology compatible with OBO-Foundry ontologies: –no overlap, –easier to re-use. Not only tracking of incidents, but also: –how well individual clinicians and organizations manage adverse events, –how well one learns from past experiences. –ontologies : realism-based view unconstrained reasoning

157. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Data warehousing 157 Werner CEUSTERS Ontology Research Group, Center of Excellence in Bioinformatics and Life Sciences Department of Psychiatry, University at Buffalo, NY, USA

158. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking based data warehousing

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

160. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Unique identifier for: –each data-element and combinations thereof (L3), –what the data-element is about (L1), –each generated copy of an existing data-element (L3), –each transaction involving data-elements (L1); Identifiers centrally managed in RTS; Exclusive use of ontologies for type descriptions following OBO-Foundry principles; Centrally managed data dictionaries, data-ownership, exchange criteria. General principles of RT-enabled data warehousing (1)

161. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U General principles of RT-enabled data warehousing (2) Central inventory of ‘attributes’ but peripheral maintenance of ‘values’; Identifiers function as pseudonyms: –centrally known that for person IUI-1 there are values about instances of UUI-2 maintained by researcher/clinician IUI-3 for periods IUI-4, IUI-5, … Disclosure of what the identifiers stand for based on need and right to know; Generation of off-line datasets for research with transaction-specific identifiers for each element.

162. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U An example: the standard approach in data analysis Cases Characteristics ch1ch2ch3ch4ch5ch6... case1 case2 case3 case4 case5 case6... phenotypicgenotypic 162

163. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The Referent Tracking approach (1) unique identification by means of ‘codes’ unique identification by means of ‘instance unique identifiers’ 163

164. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U The Referent Tracking approach (2) 164

165. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Feedback to clinical care Finding ‘similar’ patient cases: –suggestions for prevention, investigation, treatment; ‘Outbreak’ detection; Comparing outcomes; –related to disorders, providers, treatments, … Links to literature; Clinical trial selection; …

166. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Assigning IUIs to L1 and L3 #1: this lady #2: “Simpson” #3: “Smith” #4: #1’s mass #5: representation of #4’ at 2010-03-31:08.30 #7: #1’s last name #6: representation of #4’ at 2010-04-14:09.57 #8: this spreadsheet #10: format of entries in #9 #9: this column of #8 #11: owner of #8 #12: copy of #8 send to #13 …166

167. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Using IUIs #1 has-name #7 at … #2: “Simpson” #3: “Smith” #4 inheres-in #1 since … #5: representation of #4’ at 2010-03-31:08.30 #7 represented-by #2 at t 1 #6: representation of #4’ at 2010-04-14:09.57 #8: this spreadsheet #10: format of entries in #9 #9: this column of #8 #11: owner of #8 #12: copy of #8 send to #13 … #7 represented-by #3 at t 2 #4 represented by #5 since … 167