1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Ontology Seminar A common framework for representing data and what they are about April 1, 2013 – 106 Jacobs Hall, North Campus, University Buffalo, 4-6pm Werner CEUSTERS, MD Ontology Research Group, Center of Excellence in Bioinformatics and Life Sciences, Institute for Healthcare Informatics, Department of Psychiatry University at Buffalo, NY, USA http://www.org.buffalo.edu/RTU Will Hsu Neuroscience Program, School of Medicine and Biomedical Sciences, University at Buffalo, NY, USA http://www.org.buffalo.edu/RTU

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Overview Data versus what they are about Issues with data documentation and data quality tools What we need – at least – to resolve the issues –Ontological Realism –Referent Tracking A methodology explored in the OPMQoL project. 2

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics 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 3

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics A non-trivial relation ReferentsReferences 4

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics For instance: meaning and impact of changes Are differences in data about the same entities in reality at different points in time due to: –changes in first-order reality ? –changes in our understanding of reality ? –inaccurate observations ? –registration mistakes ? Ceusters W, Smith B. A Realism-Based Approach to the Evolution of Biomedical Ontologies. AMIA 2006 Proceedings, Washington DC, 2006;:121-125. http://www.referent-tracking.com/RTU/sendfile/?file=CeustersAMIA2006FINAL.pdfAMIA 2006http://www.referent-tracking.com/RTU/sendfile/?file=CeustersAMIA2006FINAL.pdf

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics What makes it non-trivial? Referents –are (meta-) physically the way they are, –relate to each other in an objective way, –follow ‘laws of nature’. References –follow, ideally, the syntactic- semantic conventions of some representation language, –are restricted by the expressivity of that language, –reference collections come, for correct interpretation, with documentation outside the representation. Window on reality restricted by: −what is physically and technically observable, −faithfulness of the ontology used, −fit between ontological commitments and computational views.

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Standard DBMS architectures are inward looking 7 http://mrbool.com/architecture-of-a-dbms/25785 A Silberschatz, HF. Korth S. Sudarshan. Database System Concepts McGraw-Hill ISBN 0-07-352332-1

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics A colleague shares his research data set 8

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics A closer look What are you going to ask him right away? What do these various values stand for and how do they relate to each other? –Might this mean that patient #5057 had only once sex at the age of 39? 9

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Documenting datasets SourcesData generationData organization Data collection sheets Instruction manuals Interpretation criteria Diagnostic criteria Assessment instruments Terminologies Data validation procedures Data dictionaries Ontologies If not used for data collection and organization, these sources can be used post hoc to document, and perhaps increase, the level of data clarity and faithfulness in and comparability of existing data collections.

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Issues with data documentation and data quality tools 11

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics The dataset’s data dictionary (codebook) 12 Field NameDescriptionTypeMissing Value RangeCoding Values idSubject idnumericnone[5033,6387] ageSubject’s ageNumericNone[14,85]Age in years sexSubject’s gender0/1none0 – male, 1 - female q3Have you had pain in the face, jaw, temple, in front of the ear or in the ear in the past month? 0/1none0 – no, 1 - yes an_8_gcps_1How would you rate your facial pain on a 0 to 10 scale at the present time, that is right now, where 0 is "no pain" and 10 is "pain as bad as could be"? numeric“.”0-100 – no pain to 10 - Pain as bad as could be an_9_gcps_2In the past six months, how intense was your worst pain rated on a 0 to 10 scale where 0 is "no pain" and 10 is "pain as bad as could be"? numeric“.”0-100 – no pain to 10 - Pain as bad as could be

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Documentation in SAS program 13

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

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Sagittal and coronal MR images of a TMJ Sommer O J et al. Radiographics 2003;23:e14-e14©2003 by Radiological Society of North America

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

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

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

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

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

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Ambiguities: are assertions about particulars or types? ‘Persistent idiopathic facial pain (PIFP)’ = ‘persistent facial pain with varying presentations …’ 21 t1t1 t2t2 t3t3 t1t1 t2t2 t3t3 t1t1 t2t2 t3t3 t1t1 t2t2 t3t3 t1t1 t2t2 t3t3 t1t1 t2t2 t3t3 persistent facial pain presentation type1 presentation type3 presentation type2 types my painhis painher pain parti- culars

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Ambiguities: are assertions about particulars or types? ‘Persistent idiopathic facial pain (PIFP)’ = ‘persistent facial pain with varying presentations …’ –if the description is about types, then the three particular pains fall under PIFP. –if the description is about (arbitrary) particulars, then only her pain falls under PIFP. 22

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Separate knowledge from what it is about. ‘13.1.2.4 Painful trigeminal neuropathy attributed to MS plaque’ ‘attributed to’ relates to somebody’s opinion about what is the case, not to what is the case. –the mistake: a feature on the side of the clinician – his (not) knowing - is taken to be a feature on the side of the patient. Similar mistakes: –‘Probable migraine’ –‘facial pain of unknown origin’ (not in ICHD). 23

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics ICHD diagnostic criteria for PIFP Persistent idiopathic facial pain (PIFP): A.Facial or oral pain for at least three months fulfilling criteria B-F B.Pain occurs daily for more than 2 hours per day C.Pain has the following features 1.Poorly localized, does not following a peripheral nerve distribution. 2.Dull, aching, nagging D.Clinical neurological examination is normal E.Simple laboratory investigations including imaging of the face and jaws exclude dental cause. F.Not better accounted for by another ICHD-III diagnosis. 24 http://ihs-classification.orghttp://ihs-classification.org (current version)

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Criteria do not replace definitions ‘13.1.1.1 Classical trigeminal neuralgia, purely paroxysmal’, has the criterion ‘at least three attacks of facial pain fulfilling criteria B-E’. This does not mean: a patient with 2 such attacks does not exhibit this type of neuralgia; It rather means: do not diagnose the patient (yet) as exhibiting this type of neuralgia. If ‘chronic pain’ is defined as ‘pain lasting longer than three months’, at what point in time starts a patient to have that type of pain? 25

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Intermediate conclusion Datasets have no value without appropriate documentation, Accurate documentation is hard to come by, Even when documentation is accurate, it is hardly machine processable. Question: would it be possible to construct self- explanatory datasets? 26

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics And old idea: Self-Identifying Data 27 Jeremy Bailey. A Self-Defining Hierarchical Data System. In: R. J. Hanisch, R. J. V. Brissenden, and J. Barnes, eds. Astronomical Data Analysis Software and Systems II. ASP Conference Series, Vol. 52, 1993

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Ontological Realism Referent Tracking What we need – at least – to resolve the issues 28

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

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics 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. 30 Smith B, Ceusters W. Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies. Applied Ontology, 2010.

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics 31 How does this painting illustrate the distinction between data and what they are about?

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics L1 - L2 L3 32 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

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics 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 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 Institute for Healthcare Informatics BFO 2.0 continuants (April 1, 2013) Independent continuant Material entity Object Object aggregate Fiat object part Immaterial entity Continuant fiat boundary Site Spatial region Specifically dependent continuant Quality Relational quality Realizable entity Role (externally-grounded realizable entity) Disposition (internally-grounded realizable entity) Function Generically dependent continuant 34

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics BFO 2.0 occurrents (April 1, 2013) process complete process history sectional process process profile process boundary temporal region zero-dimensional temporal region one-dimensional temporal region spatiotemporal region 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 Institute for Healthcare Informatics Between ‘generic’ and ‘specific’ L1. First-order reality L2. Beliefs (knowledge) GenericSpecific DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MIGRAINE HEADACHE PERSON DISEASE PATHOLOGICAL STRUCTURE PAIN DRUG me my headache my migraine my doctor my doctor’s computer L3. Representation pain classificationEHR ICHDmy EHR Referent TrackingBasic Formal Ontology GenericSpecific 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 Institute for Healthcare Informatics 37 tt t instanceOf The essential pieces material object spacetime region me some temporal region my life my 4D STR some spatial region history spatial region temporal region dependent continuant some quality located-in at t … at t participantOf at toccupies projectsOn projectsOn at t

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Should be obvious for ontologists, but … Comments by ICBO 2013 reviewers: (with self-claimed ‘high confidence’ in their expertise) –There is a problem with the relation ‘X instance-of Y at time t’, because the time-index ‘t’ remains unclear. is the restriction of a continuant to a proper time segment of the life-time a continuant too? Is the restriction of a continuant to a time-point, a continuant again?  unclarity is in the eyes of those who look only through (OWL-)DL glasses –This paper describes a mechanism for […] into relationships of the form: x r1 y r2 t, where x i[s a]n individual, y is a term, and t is a time.  confuses terms with what they are about –… the uniqueness of the entities behind #1, #1, #3 can only be derived from the whole description.  never bothered to read any papers about this topic 38

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Fundamental goals of Referent Tracking Who remembers?

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics explicit reference to the concrete individual entities relevant to the accurate description of some portion of reality,... Fundamental goals of Referent Tracking Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

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

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics 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 (Indep. cont.)#2: #1’s yellow (Quality) 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

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics The shift envisioned From: –‘this man is a 40 year old patient with molar caries’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf caries… this-4 partOf this-5 … this-5 instanceOf molar… this-5 partOf this-1 … …

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics The shift envisioned denotators for particulars From: –‘this man is a 40 year old patient with molar caries’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf caries… this-4 partOf this-5 … this-5 instanceOf molar… this-5 partOf this-1 … …

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics The shift envisioned denotators for appropriate relations From: –‘this man is a 40 year old patient with molar caries’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf caries… this-4 partOf this-5 … this-5 instanceOf molar… this-5 partOf this-1 … …

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics The shift envisioned denotators for universals or particulars From: –‘this man is a 40 year old patient with molar caries’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf caries… this-4 partOf this-5 … this-5 instanceOf molar… this-5 partOf this-1 … …

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics The shift envisioned time stamp in case of continuants From: –‘this man is a 40 year old patient with molar caries’ To (something like) : –‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years… this-2 qualityOf this-1… this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf caries… this-4 partOf this-5 … this-5 instanceOf molar… this-5 partOf this-1 … …

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

49. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Should be obvious for ontologists, but … Comments by ICBO 2013 reviewers: (with self-claimed ‘high confidence’ in their expertise) –The basic idea is to introduce unique identifiers for denoting the real world entities. This notion is very similar to the URI (uniform resource identifier), used in RDF and the semantic web.  Rather just a bit similar but WITH a more precise semantics –All together we achieve a sentence F(#1,#2,#3) with three constants, denoting real world entities. Since a direct link between #i and a real entity does not solve the problem of uniqueness, the uniqueness of the entities behind #1, #1, #3 can only be derived from the whole description. 49

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Should be obvious for ontologists, but … The idea of denoting entities by UII is trivial, and is already used within the framework of RDF and the semantic web. However: for RDF –No distinction between classes and instances (individuals) –Properties can themselves have properties –No distinction between language constructors and ontology vocabulary, so constructors can be applied to themselves/each other  No ontological foundations 50

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Towards self-explanatory datasets in OPMQoL 51

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Specific Aims of the OPMQoL Project 1.describe the portions of reality covered by the five datasets by means of a realism-based ontology (OPMQoL), 2.design bridging axioms required to express the data dictionaries of the datasets in terms of the OPMQoL and translate these axioms in the query languages used by the underlying databases, 3.validate OPMQoL by querying the datasets with and without using the ontology and by comparing the results in function of the clinical question identified, 4.document the development and validation approach in a way that other groups can re-use and expand OPMQoL, and use our approach in other domains. 52

53. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Considered datasets ‘US Dataset’ (724 patients) resulted from the NIH funded RDC/TMD Validation Project, ‘Hadassah Dataset’ (306 patients) from the Orofacial Pain Clinic at the Faculty of Dentistry, Hadassah, ‘German Dataset’ (416 patients) of patients seeking treatment for orofacial pain at the Department of Prosthodontics and Materials Sciences, University of Leipzig, ‘Swedish Dataset’of 46 consecutive Atypical Odontalgia (AO) patients recruited from 4 orofacial pain clinics in Sweden as well as data about age- and gender-matched control patients, 35 of which being painless and 41 being TMD patients, ‘UK Dataset’ (168 patients) of facial pain of non dental origin present for a minimum of three months. 53

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

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

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

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

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

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

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

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

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

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

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

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

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

68. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Methodology (2): for each dataset Build a formal template which describes: –the results of steps 4-6 of the 1 st order analysis, –the relationships between: the 1 st order entities and the corresponding data items in the data set, data items themselves. Build a prototype able to generate on the basis of the template for each subject (patient) in the dataset an RT-compatible representation of his 1 st and 2 nd order entities. 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 Institute for Healthcare Informatics The template 69

70. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Partial Template for 3 variables (in the ‘German’ dataset) 70 RNVarRTREFMinMaxVal 1IMpatient_study_record 2idLVpatient_identifier 3idIMpatient 4sexCVgender 5sexCVmale0 6sexCVfemale1 7sexUAsexBLANK 8q3CVno_pain_in_ lower_face0 9q3CVpain_in_ lower_face1 10q3IMin_the_past_month 11q3IMlower_face 12q3IMtime_of_q3_concretization 13q3RPan_8_gcps_1000 14q3UPan_8_gcps_11100 15q3UAan_8_gcps_1BLANK 1 16q3JAan_8_gcps_1BLANK 0

71. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics 3 variables in the ‘German’ dataset 71 RNVarRTREFMinMaxVal 1IMpatient_study_record 2idLVpatient_identifier 3idIMpatient 4sexCVgender 5sexCVmale0 6sexCVfemale1 7sexUAsexBLANK 8q3CVno_pain_in_ lower_face0 9q3CVpain_in_ lower_face1 10q3IMin_the_past_month 11q3IMlower_face 12q3IMtime_of_q3_concretization 13q3RPan_8_gcps_1000 14q3UPan_8_gcps_11100 15q3UAan_8_gcps_1BLANK 1 16q3JAan_8_gcps_1BLANK 0 Answer to the question: ‘Have you had pain in the face, jaw, temple, in front of the ear or in the ear in the past month?’ Answer to the question: ‘’ How would you rate your facial pain on a 0 to 10 scale at the present time, that is right now, where 0 is "no pain" and 10 is "pain as bad as could be"?

72. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Record Types in the template 72 RNVarRTREFMinMaxVal 1IMpatient_study_record 2idLVpatient_identifier 3idIMpatient 4sexCVgender 5sexCVmale0 6sexCVfemale1 7sexUAsexBLANK 8q3CVno_pain_in_ lower_face0 9q3CVpain_in_ lower_face1 10q3IMin_the_past_month 11q3IMlower_face 12q3IMtime_of_q3_concretization 13q3RPan_8_gcps_1000 14q3UPan_8_gcps_11100 15q3UAan_8_gcps_1BLANK 1 16q3JAan_8_gcps_1BLANK 0 LV: Literal value CV: Coded Value IM: Implicit JA: Justified Absence UA: Unjustified Absence UP: Unjustified Presence RP: Redundant Presence

73. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Condition-based xA/xP determination 73 RNVarRTREFMinMaxVal 7sexUAsexBLANK 13q3RPan_8_gcps_1000 14q3UPan_8_gcps_11100 15q3UAan_8_gcps_1BLANK 1 16q3JAan_8_gcps_1BLANK 0 If the value of REF is either outside the range of Min/Max or ‘BLANK’ and the value for Var is as indicated by Val, including no value at all, then the presence or absence of the corresponding data item is of a sort indicated by RT.

74. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Conditional selection of descriptions 74

75. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics RT compatible part of the template 75 RNIUI(L)IUI(P)P-TypeP-RelP-TargTrelTime 1#psrec- DATASET - RECORD att 2#pidL-#pid- DENOTATOR denotes#pat-att 3#patL-#pat- PATIENT att 4#patgL-#patg- GENDER inheres-in#pat-att 5#patg- MALE - GENDER inheres-in#pat-att 6#patg- FEMALE - GENDER inheres-in#pat-att 7#patgL- UNDERSPEC - ICE att 8#q3L0-#pat-lacks-pcp PAIN at#tq3- 9#q3L1-#pq3- PAIN participant#pat-at#tq3- 10#tq3- MONTH - PERIOD 11#patlf- LOWER - FACE part-of#pat-att 12#cq3- TIME - PERIOD after#tq3- 13#q3L- corresp-w#q3L0-att 14#q3L- DISINFORMATION att 15#q3L- UNDERSPEC - ICE att 16#q3L- J - BLANK - ICE att

76. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics RT compatible part of the template 76 RNIUI(L)IUI(P)P-TypeP-RelP-TargTrelTime 1#psrec- DATASET - RECORD att 2#pidL-#pid- DENOTATOR denotes#pat-att 3#patL-#pat- PATIENT att 4#patgL-#patg- GENDER inheres-in#pat-att 5#patg- MALE - GENDER inheres-in#pat-att 6#patg- FEMALE - GENDER inheres-in#pat-att 7#patgL- UNDERSPEC - ICE att 8#q3L0-#pat-lacks-pcp PAIN at#tq3- 9#q3L1-#pq3- PAIN participant#pat-at#tq3- 10#tq3- MONTH - PERIOD 11#patlf- LOWER - FACE part-of#pat-att 12#cq3- TIME - PERIOD after#tq3- 13#q3L- corresp-w#q3L0-att 14#q3L- DISINFORMATION att 15#q3L- UNDERSPEC - ICE att 16#q3L- J - BLANK - ICE att denotes (when instantiated) the gender of the patient denotes (when instantiated) the data item concretized in the dataset in relation to the gender of the patient

77. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics RT compatible part of the template 77 RNIUI(L)IUI(P)P-TypeP-RelP-TargTrelTime 1#psrec- DATASET - RECORD att 2#pidL-#pid- DENOTATOR denotes#pat-att 3#patL-#pat- PATIENT att 4#patgL-#patg- GENDER inheres-in#pat-att 5#patg- MALE - GENDER inheres-in#pat-att 6#patg- FEMALE - GENDER inheres-in#pat-att 7#patgL- UNDERSPEC - ICE att 8#q3L0-#pat-lacks-pcp PAIN at#tq3- 9#q3L1-#pq3- PAIN participant#pat-at#tq3- 10#tq3- MONTH - PERIOD 11#patlf- LOWER - FACE part-of#pat-att 12#cq3- TIME - PERIOD after#tq3- 13#q3L- corresp-w#q3L0-att 14#q3L- DISINFORMATION att 15#q3L- UNDERSPEC - ICE att 16#q3L- J - BLANK - ICE att

78. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Work in progress: IAO (?) related types UNDERSPECIFIED-ICE –ICE which describes a portion of reality at determinable rather than determinate level DISINFORMATION –GDC which provides erroneous information J-BLANK-ICE –GDC which conveys there should not be an ICE concretized. 78

79. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Acknowledgement The work described is funded in part by grant 1R01DE021917-01A1 from the National Institute of Dental and Craniofacial Research (NIDCR). The content of this presentation is solely the responsibility of the author and does not necessarily represent the official views of the NIDCR or the National Institutes of Health.