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 MHI501 – Introduction to Health Informatics The UMLS, SNOMED and the NCI Thesaurus (just to list a few, and in the first place … outstanding problems!) SUNY at Buffalo - November 7, 2007 Werner CEUSTERS Center of Excellence in Bioinformatics and Life 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 The problem to be solved

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 3 A general belief: Better information Better care

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 4 ‘Information’ versus ‘informing’ Better information Better care Being better informed

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 5 A general belief:Being better informed Concerns primarily the delivery of information: Better information Better care Being better informed

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 6 A general belief:Being better informed Concerns primarily the delivery of information: –Timely, –Where required (e.g. bed-side computing), –What is permitted, –What is needed. Involves: –Connecting systems, –Making systems interoperable: Syntactically, Semantically.

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 7 HIMSS Integration and Interoperability Steering Committee the ability of health IS to work together within and across organizational boundaries in order to advance the effective delivery of healthcare for individuals and communities, covering the following dimensions: –Uniform movement of healthcare data, –Uniform presentation of data, –Uniform user controls, –Uniform safeguarding data security and integrity, –Uniform protection of patient confidentiality, –Uniform assurance of a common degree of system service quality. Interoperability Definition and Background. Approved by HIMSS Board of Directors., 06/09/05.

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 8 HIMSS Integration and Interoperability Steering Committee the ability of health IS to work together within and across organizational boundaries in order to advance the effective delivery of healthcare for individuals and communities, covering the following dimensions: –Uniform movement of healthcare data, –Uniform presentation of data, –Uniform user controls, –Uniform safeguarding data security and integrity, –Uniform protection of patient confidentiality, –Uniform assurance of a common degree of system service quality. No mention of information quality Interoperability Definition and Background. Approved by HIMSS Board of Directors., 06/09/05.

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 9 Ontolog-Discussion: Healthcare Informatics Landscape “The Business Value for Health IT Ontology Tools in Health Data and Information Systems: Facilitates development of open-standards, interoperable networks of health information systems and EHRs, Supports patient safety and goals to reduce medical errors in health care delivery, Promotes data quality in the electronic exchange of health information.” Marc Wine, August 25, 2005 Is about quality preservation

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 10 “Better Information” must cover … EHR PHR Various modality related databases –Lab, imaging, … Classification systems Terminologies Ontologies Textbooks Patient-specific information Medical “knowledge”

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 11 How to assess whether information is “better” ? Coverage Authority Objectivity Accuracy Timeliness Utility Understandability Seems to have received most attention thus far

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 12 number of unique medical expressions: 10 7 –In one domain (AIDS) : 150.000 candidate term phrases of 1 to 5 words found –100-200 subdomains in medicine estimated 2-word expressions: 4*10 6 –assumes 20.000 meaningful single words –assumes 10% combination rate Some figures about the estimated size of “clinical language” (Evans & Patel ‘91)

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 13 0.5 x 10 6 entries in Oxford Dictionary of English 0.3 x 10 6 word occurrences in Snomed 3.1 0.15 x 10 6 meanings in Meta-1.3 0.10 x 10 6 entries in Dorland’s Medical Dictionary 0.05 x 10 6 entries in Webster’s Collegiate Dict. 0.01 x 10 6 words in average human recognition voc. 0.005 x 10 6 words in “basic English” Some figures about the estimated size of “clinical language” (Tuttle & Nelson ‘94)

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 14 Main purposes: to stabilize the terminology Mechanism: assign a code to every single term Uses: –EDI –data storage and archiving –NLP Disadvantages: –no internal structure –difficulties in finding specific terms –does not account for synonyms Coding systems and nomenclatures in healthcare

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 15 Ontologies Terminologies More advanced ‘semantic’ technologies Coverage Authority Objectivity Accuracy Timeliness Utility Understandability Coding & classification systems

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 On ‘meaning’ and ‘understanding’

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 17 What is understanding ? To understand something is to know what its significance is. What 'knowing significance' amounts to may be very different in different contexts: thus understanding a piece of music requires different things of us than understanding a sentence in a language we are learning, for instance. It would be useful, then, for theorists to look at the different kinds of understanding that there are, and examine them in detail and without prejudice, rather than looking for the essence of understanding. ( Tim Crane, philosopher of mind ) The significance of a single sentence, too, can vary from context to context.

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 18 The etymology of “understanding” “understanding”  Latin “substare” –literally: “to stand under” Websters Dictionary (1961) understanding = the power to render experience intelligible by bringing perceived particulars under appropriate concepts. “particulars” = what is NOT SAID of a subject (Aristotle) –substances: this patient, that tumor,... –qualities: the red of that patient’s skin, his body temperature, blood pressure,... –processes: that incision made by that surgeon, the rise of that patient’s temperature,... “concepts”: may be taken in the above definition as Aristotle’s “universals” = what is SAID OF a subject –Substantial concepts: patient, tumor,... –Quality concepts: white, temperature –...

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 19 Understanding natural language Is constructing meaning from language by which the degree of understanding involves a multifaceted meaning-making process that depends on knowledge about language and knowledge about the world. ( cf. “reading comprehension” by humans. ) But then: what is “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 20 Dyadic models of “meaning” Saussure ( language philosopher ): –signe / signifiant(sign/concept) Ron Stamper ( information scientist ): –thing-A STANDS-FOR thing-B Major drawback: –excludes the “referent” from the model, i.e. that what the sign/symbol/word/... denotes

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 21 Standard Semiotic/Semantic Triangle

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 22 Triadic models of meaning: The Semiotic/Semantic triangle Sign: Language/ Term/ Symbol Referent: Reality/ Object Reference: Concept / Sense / Model / View / Partition

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 23 Aristotle’s triadic meaning model semeia gramma/ phoné pragma pathema Words spoken are signs or symbols (symbola) of affections or impressions (pathemata) of the soul (psyche); written words (graphomena) are the signs of words spoken (phoné). As writing (grammatta), so also is speech not the same for all races of men. But the mental affections themselves, of which these words are primarily signs (semeia), are the same for the whole of mankind, as are also the objects (pragmata) of which those affections are representations or likenesses, images, copies (homoiomata). Aristotle, 'On Interpretation', 1.16.a.4-9, Translated by Cooke & Tredennick, Loeb Classical Library, William Heinemann, London, UK, 1938.

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 24 Richards’ semantic triangle Reference (“concept”): “indicates the realm of memory where recollections of past experiences and contexts occur”. Hence: as with Aristotle, the reference is “mind- related”: thought. But: not “the same for all”, rather individual mind- related symbolreferent reference understandingmy your understanding

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 25 Don’t confuse with homonymy ! “mole” mole “animal” R1 mole “unit” R2 mole “skin lesion” R3

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 26 Different thoughts Homonymy “ mole ” mole “ animal ” R1 mole “ unit ” R2 mole “ skin lesion ” R3 symbol referent understanding One concept of x understanding of y

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 27 And by the way, synonymy... the Aristotelian viewRichards’ view “perspiration” “sweat” “perspiration”

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 28 Frege’s view “sense” is an objective feature of how words are used and not a thought or concept in somebody’s head 2 names with the same reference can have different senses 2 names with the same sense have the same reference (synonyms) a name with a sense does not need to have a reference (“Beethoven’s 10 th symphony”) reference (=referent) sense name

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 29 But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort Most terminologies are ‘concept’-based 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 30 But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort These views require the involvement of a cognitive entity: Most terminologies are ‘concept’-based

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 31 But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: meaning shared in common by synonymous terms idea shared in common in the minds of those who use these terms unit of describing meanings knowledge universal that what is shared by all and only all entities in reality of a similar sort These views require the involvement of a cognitive entity: This view does not presuppose cognition at all Most terminologies are ‘concept’-based

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 32 Concept as mental particulars: Seth Russell’s ‘mentography’ http://robustai.net/mentography/Mentography.html (Just for reference, I didn’t study this in detail, so I make no statement about the value of this approach)

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 ‘Concept’ * -orientation has sad consequences Too much effort goes into the specification business –OWL, DL-reasoners, translators and convertors, syntax checkers,... Too little effort into the faithfulness of the conceptualizations towards what they represent. –Pseudo-separation of language and entities “absent nipple”, “planned act”, “prevented abortion” Many concept-based systems exhibit mistakes of various sorts. * When ‘concept’ is not clearly defined

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 34 Some examples Gene Ontology – menopause part_of death * SNOMED – both uterii is_a uterus * UMLS – blood pressure is_a lab result GALEN – vomitus contains carrot *corrected in most recent version

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 35 Another known problem: Intentionality in the semiotic triangle “The physician wanted to give the patient an injection” The physician gave the injection (= referent), and because of that, the patient died from a side-effect. Hence: “giving the injection” = “killing the patient” (= two references) Hence??? –“the physician wanted to kill the patient” ???

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 36 Take home message Terminology: to find your way in language Ontology: to find your way in what there is Terminology + ontology: –to accurately describe what there is –to get an accurate picture of what there is on the basis of descriptions. This works only if ontologies and terminologies exhibit a sound, consistent and coherent structure. Most of them don’t !

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 37 The word ‘Ontology’ has two meanings Ontology: the science of what entities exist and how they relate to each other. An ontology: a representation of some domain which –(1) is intelligible to a domain expert, and –(2) is formalized in a way that allows it to support automatic information processing.

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 38 Within the context of ‘an ontology’, the word ‘domain’ has two meanings For most computer scientists: –A representation of an agreed upon conceptualization about which man and machine can communicate using an agreed upon vocabulary For philosophical ontologists: –A representation of a portion of reality Still allowing for a variety of entities to be recognised by one school and refuted by another one

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 39 Structure of remaining part of this talk Discuss several well-known coding systems, classifications, terminologies, etc, … Students should try to find the gazillion ways in which the principles of a coherent language-reality model are violated. Since there are a gazillion violations, it should not be too difficult to find many. Therefore, I make it more challenging by not listing the principles first.

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 An easy starter: Border’s classification of ‘medicine’

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 41 Border’s classification of medicine: what’s wrong ? Medicine –Mental health –Internal medicine Endocrinology –Oversized endocrinology Gastro-enterology... –Pediatrics –... –Oversized medicine Refer to the size of the books that do not fit on a normal Border’s Bookshop shelf

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 MeSH: Medical Subject Headings

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 43 MeSH

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 44 Use of MeSH in PUBMED Wolfram syndrome

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 45 MeSH: Medical Subject Headings Designed for bibliographic indexing, eg Index Medicus Basis for MedLINE  Pubmed focuses on biomedicine and other basic healthcare sciences clinically very impoverished Consistency amongst indexers: –60% for headings –30% for sub-headings

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 46 MeSH Tree Structures - 2004 1. Anatomy [A] 2. Organisms [B] 3. Diseases [C] 4. Chemicals and Drugs [D] 5. Analytical, Diagnostic and Therapeutic Techniques and Equipment [E] 6. Psychiatry and Psychology [F] 7. Biological Sciences [G] 8. Physical Sciences [H] 9. Anthropology, Education, Sociology and Social Phenomena [I] 10. Technology and Food and Beverages [J] 11. Humanities [K] 12. Information Science [L] 13. Persons [M] 14. Health Care [N] 15. Geographic Locations [Z]

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 47 Cardiovascular Diseases [C14] – Heart Diseases [C14.280] Arrhythmia [C14.280.067] + Carcinoid Heart Disease [C14.280.129] Cardiomegaly [C14.280.195] + Endocarditis [C14.280.282] + Heart Aneurysm [C14.280.358] Heart Arrest [C14.280.383] + Heart Defects, Congenital [C14.280.400] – Aortic Coarctation [C14.280.400.090] – Arrhythmogenic Right Ventricular Dysplasia [C14.280.400.145] – Cor Triatriatum [C14.280.400.200] – Coronary Vessel Anomalies [C14.280.400.210] – Crisscross Heart [C14.280.400.220] – Dextrocardia [C14.280.400.280] + MeSH Tree Structures – 2004

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 48 What is the problem ? (MeSH 2007) ? ? Different set of more specific terms when different path from the top is taken.

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 49 MeSH: some paths from top to Wolfram Syndrome Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Diseases Optic Atrophy Optic Atrophies, Hereditary Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus Female Urogenital Diseases and Pregnancy Complications Female Urogenital Diseases

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 50 What would it mean if used in the context of a patient ? Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Diseases Optic Atrophy Optic Atrophies, Hereditary has Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Female Urogenital Diseases and Pregnancy Complications Female Urogenital Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus ??? … has

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 51 Principle If a particular (individual) is related in a specific way to a ‘class’, it should also be related in the same way to all the ‘superclasses’ of that class –Technically: “… to all the classes that subsume that class”

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 52 What would it mean if used under Referent Tracking ? Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Diseases Optic Atrophy Optic Atrophies, Hereditary has Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Female Urogenital Diseases and Pregnancy Complications Female Urogenital Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus #x isa … Referent tracking ??? How can something which is an eye disease be a urologic disease ?

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 53 Principle If a particular is an instance of a ‘class’, it should also be an instance of all the ‘superclasses’ of that class

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 54 Time issue in assigning particulars to categories caterpillarbutterfly animal t human being living creature me child Instance-of in 1960 adult me Instance-of since 1980

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 Part-of can be generalized, … with care ! me human being Instance-of at t living creature Is_a my left leg part-of at t leg Instance-of at t Horse legs are not parts of human beings Amputated legs are not parts of human beings ‘Canonical leg is part of canonical human being’, but…, there are (very likely) no such particulars … Part-of ?

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 Body Regions [A01] –Extremities [A01.378] Lower Extremity [A01.378.610] –Buttocks [A01.378.610.100] –Foot [A01.378.610.250] »Ankle [A01.378.610.250.149] »Forefoot, Human [A01.378.610.250.300] + »Heel [A01.378.610.250.510] –Hip [A01.378.610.400] –Knee [A01.378.610.450] –Leg [A01.378.610.500] –Thigh [A01.378.610.750] What’s wrong ? MeSH Tree Structures – 2007

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 57 Body Regions [A01] – Abdomen [A01.047] + – Back [A01.176] + – Breast [A01.236] + – Extremities [A01.378] Amputation Stumps [A01.378.100] Lower Extremity [A01.378.610] + Upper Extremity [A01.378.800] + – Head [A01.456] + – Neck [A01.598] – Pelvis [A01.673] + – Perineum [A01.719] – Thorax [A01.911] + – Viscera [A01.960] MeSH Tree Structures – 2007 What’s wrong ?

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 SNOMED: Systematized Nomenclature of Medicine

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 59 SNOMED’s origin: SNOP from CAP (1965)

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 60 Since mid 2007

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 61 SNOMED International (1995) Multi-axial coding system: –morphology, disease, function, procedure,... Each axis has an hierarchical structure Translations in other languages than English only for older versions Informal internal structuring Being translated in CG formalism, but with only internal consistency Possibility to generate meaningless concepts Mixing of hierarchies: –Bone Long Bone Periosteum Shaft

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 62 Snomed International (1995) Number of records (V3.1) TTopography12,385 MMorphology 4,991 FFunction16,352 LLiving Organisms24,265 CDrugs &Biological Products14,075 APhysical Agents, Forces and Activities 1,355 DDisease/ Diagnosis28,623 PProcedures27,033 SSocial Context 433 JOccupations 1,886 GGeneral Modifiers 1,176 TOTAL RECORDS 132,641

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 63 Evolution of SNOMED descriptions

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 64 Term additions and deletions since 20020731

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 65 Number of changes over time 100%1,042,871100%373,731Total 0.00%0 18 2 07 3 36 430.02%745 0.17%1,7280.54%2,0304 1.23%12,8715.34%19,9723 15.20%158,48832.35%120,9132 83.40%869,73661.74%230,7381 %N%NN DescriptionsClassesMods

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 66 Snomed International (1995): knowledge in the codes. posterior anatomic leaflet mitral cardiac valve cardiovascular T-23532 Why was this not a good idea ?

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 67 Snomed International : multiple ways to express the same thing D5-46210Acute appendicitis, NOS D5-46100Appendicitis, NOS G-A231Acute M-41000Acute inflammation, NOS G-C006In T-59200Appendix, NOS G-A231Acute M-40000Inflammation, NOS G-C006In T-59200Appendix, NOS

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 68 Using a formal language SNOMED RT syntax: D5-30150: D5-30100 & (assoc-topography T-56000) & (assoc-morphology M-40000) & (assoc-etiology F-06030) (T-56000)(M-40000)(F-06030) Parent term in the SNOMED III hierarchy: D5-30100 Esophagitis, NOS D5-30150 Postoperative esophagitis T-56000 Esophagus M-40000 Inflammation F-06030 Post-operative state SNOMED III:

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 69 SNOMED CT: 20070131 Body structure Clinical finding Environment or geographical location Event Linkage concept Observable entity Organism Pharmaceutical / biologic product Physical force Physical object Procedure Qualifier value Record artifact Situation with explicit context Social context Special concept Specimen Staging and scales Substance 373,731 ‘concepts’ Problems ?

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 70 Find the problem SNOMED-RT (2000) SNOMED-CT (2003) DL don’t guarantee you to get parthood right !

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 71 Find the problem

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 72 Find the problem new-1 new-2

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 73 Inconsistent response to queries Term search: heart tumor Concept search: heart structure AND tumor

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 74 Find the problem What is the problem ? Missing: ISA neoplasm of heart

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 75 Find the problem terms

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 Find the problem No ! Although suggested, that is not what is expressed. Can there be something that is an excision and an implantation ? Does “testis implantation” mean that a testis is implanted ?

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 77 Find the problem

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 78 Find the problem

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 79 Find the problem

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 80 Find the problem

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 81 Uncorrected lexical mapping

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 82 Find the problem

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 83 Find the problem

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 84 Find the problem

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 85 Find the problem

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 86 Snomed CT (July 2007): “fractured nasal bones”

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 87 SNOMED-CT: abundance of false synonymy nose bones fracture

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 88 Coding / Classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

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 89 A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = = Coding / Classification confusion A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = =

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 90 Snomed CT (July 2007): “fractured nasal bones”

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 91 Snomed CT (July 2007): “fractured nasal bones” Problems of multiple inheritance: – (1) “… ISA fracture of skull and facial bones” Which facial bones are not part of the skull ? If there would be non-skull facial bones, how many fractures are then required ? –(2) “… ISA fracture of mid-facial bones” Which mid-facials bones or not facial bones ? –If all, then (1) is redundant –(3) “… ISA injury of nasal bones” Are not all fractures “injuries’ and if not, why would then all nasal fractures be injuries ?

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 92 Find the problem

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 UMLS: Unified Medical Language System

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 94 UMLS: Unified Medical Language System Tool for information retrieval of 4 components: –Metathesaurus contains information about biomedical concepts and how they are represented in diverse terminological systems. –Semantic Network contains information about concept categories and the permissible relationships among them –Information Sources Map contains both human-readable and machine-processable information about all kinds of biomedical terminological systems –Specialist lexicon: english words with POS

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 95 UMLS Semantic Network

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 96 Semantic Network Relationships Is_a physically related to spatially related to temporally related to functionally related to conceptually related to

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 97 Semantic Network “Biologic Function” Hierarchy

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 98 Semantic Network "affects" Hierarchy

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 99 Metathesaurus: merging terminologies cycles in hierarchical relationships

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 NCI Thesaurus National Cancer Institute

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 101 NCI Thesaurus a biomedical thesaurus created specifically to meet the needs of the National Cancer Institute. semantically modeled cancer-related terminology built using description logics

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 102 NCI Thesaurus Root concepts Anatomic Structure, Anatomic System, or Anatomic Substance ? Or ? Does the NCI not know to which category Any item classified there belongs ? Anatomic Substance ? If yes, why is gene product not subsumed by it ? If no, why are drugs and chemicals not subsumed by it ?

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 103 Conceptual entity Definition: none Semantic type: –Conceptual entity –Classification Subconcepts: –Action: definition: action; a thing done –And: Definition: an article which expresses the relation of connection or addition, used to conjoin a word with a word,... –Classification Definition: the grouping of things into classes or categories

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 104 Definition of “cancer gene”

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 105 NCI Thesaurus architecture Disease BreastBreast neoplasm Disease-has-associated-anatomy ISA Findings-And- Disorders-Kind Anatomy-Kind “Formal subsumption” or “inheritance” “Associative” relationships providing “differentiae” “Kinds” restrict the domain and range of associative relationships What diseases have a diameter of over 3 cm ?

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 106 Being critical ≠ being negative RFQ-NCI-60001-NG: Review of NCI Thesaurus and Development of Plan to Achieve OBO-Compliance Grant to Apelon (H. Solbrig) to improve NCIT

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 A few other systems

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 108 MEDCIN Inappropriate label for out of context reading Agrammatical constructions for labels Unexpected shift of ontological category

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 109 MeDDRA For MeDDRA: a viral meningitis is not a meningitis

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 HL7 RIM: Health Level 7 Reference Information Model

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 111 THOU SHALL NOT CONFUSE … information representation with domain representation data are about observables, but are not observables Information about X part_of information about Y X part of Y

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 112 HL7-RIM Animal Definition: A subtype of Living Subject representing any animal-of-interest to the Personnel Management domain. LivingSubject Definition: A subtype of Entity representing an organism or complex animal, alive or not. Smith B, Ceusters W. HL7 RIM: An Incoherent Standard, Stud Health Technol Inform. 2006;124:133-138.

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 113 Fundamental mistake in HL7 RIM Act as statements or speech-acts are the only representation of real world facts or processes in the HL7 RIM. The truth about the real world is constructed through a combination (and arbitration) of such attributed statements only, and there is no class in the RIM whose objects represent "objective state of affairs" or "real processes" independent from attributed statements. As such, there is no distinction between an activity and its documentation. HL7 Reference Information Model V 02-14n 11/1/2006 - Basis for Normative Edition 2007 Retrieved Oct 20, 2007 from http://www.hl7.org/Library/data-model/RIM/C30214n/rim0214nc.zip

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 114 Thus: watching sports is as good as doing sports HL7 as causal factor in pandemic obesity

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 115 AdverseEvent (BRIDG logical model p168, HE!) Type: Class Assessment Status: Proposed. Version 1.0. Phase 1.0. Package: Clinical Research Activities Keywords: Detail: Created on 05/24/2006. Last modified on 01/26/2007. GUID: {CD620136-3CB9-4382-802B-F6CA82F98C10} An observation of a change in the state of a subject that is assessed as being untoward by one or more interested parties within the context of protocol-driven research or public health.

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 Are we done ? Is an accurate coding system, classification system, terminology, ontology, …, a necessary and sufficient condition for obtaining “better” information ? Necessary: yes ! Sufficient: no !

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 117 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateObsCodeNarrative 093920/12/1998255087006malignant polyp of biliary tract Three references of hypertension for the same patient denote three times the same disease. If two different fracture codes are used in relation to observations made on the same day for the same patient, they might refer to the same fracture The same type of location code used in relation to three different events might or might not refer to the same location. If the same fracture code is used for the same patient on different dates, then these codes might or might not refer to the same fracture. The same fracture code used in relation to two different patients can not refer to the same fracure. If two different tumor codes are used in relation to observations made on different dates for the same patient, they may still refer to the same tumor. Using codes does not prevent ambiguities as to what is described: how many disorders are listed?

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 118 Consequences Very difficult to: –Count the number of (numerically) different diseases Bad statistics on incidence, prevalence,... Bad basis for health cost containment –Relate (numerically the same or different) causal factors to disorders: –Dangerous public places (specific work floors, swimming pools), –dogs with rabies, –HIV contaminated blood from donors, –food from unhygienic source,... Hampers prevention –...

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 Referent Tracking

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 120 Purpose: –explicit reference to the concrete individual entities relevant to the accurate description of each patient’s condition, therapies, outcomes,... Proposed Solution: Referent Tracking Now! That should clear up a few things around here ! Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

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 121 Proposed Solution: Referent Tracking Numbers instead of words! Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78. Method: –Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity 78

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 122 ‘John Doe’s ‘John Smith’s liver tumor was treated with RPCI’s irradiation device’ ‘John Doe’s liver tumor was treated with RPCI’s irradiation device’ The principle of Referent Tracking #1 #3 #2 #4 #5 #6 treating person liver tumor clinic device instance-of at t 1 #10 #30 #20 #40 #5 #6 inst-of at t 2 inst-of at t 2 inst-of at t 2 inst-of at t 2 inst-of at t 2

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 123 EHR – Ontology “collaboration”

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 124 Advantage: better reality representation 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateObsCodeNarrative 093920/12/1998255087006malignant polyp of biliary tract IUI-001 IUI-003 IUI-004 IUI-005 IUI-007 IUI-002 IUI-012 IUI-006 7 distinct disorders

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 Coping with change

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 126 Accept that everything may change: 1.changes in the underlying reality: Particulars and universals come and go 2.changes in our (scientific) understanding: The planet Vulcan does not exist 3.reassessments of what is considered to be relevant for inclusion (notion of purpose). 4.encoding mistakes introduced during data entry or ontology development.

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 127 Key requirement for updating Any change in an ontology or EHR should be associated with the reason for that change to be able to assess later what kind of mistake has been made !

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 128 Example: a person (in this room) ’s gender in the EHR In John Smith’s EHR: –At t 1 : “male”at t 2 : “female” What are the possibilities ? Change in reality: transgender surgery change in legal self-identification Change in understanding: it was female from the very beginning but interpreted wrongly Correction of data entry mistake: it was understood as male, but wrongly transcribed

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 129 Reality versus belief, both in evolution t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 p4: an artifact on an image p5: a coin lesion that really corresponds with John’s tumor (IUI-#5): acknowledgement of the referential nature of p5

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 130 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 In the beginning, there was nothing …

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 131 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 Evolution (or some creative designer) brings benign tumors to existence, but we, poor humans, don’t know that yet…

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 132 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 The existence of benign tumors is acknowledged, but malignancy not yet …

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 133 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 We know about malignancy, a growth in John Doe, benign, came about, but we are not aware of it. Malignancy has been discovered.

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 134 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John consulted a physician, a picture is taken, it shows in reality a lesion, but it is not perceived.

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 135 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John’s tumor is being discovered, but that it turned malignant, remains unnoticed.

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 136 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 A second image is taken, that image shows a lesion that is correctly perceived, and allows to make the diagnosis of malignancy.

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 137 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John’s tumor is treated by means of RPCI’s irradation device, and wrongly believed to have disappeared.

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 138 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 John is lucky: his tumor indeed disappeared. His physician is lucky: he escapes a maltreatment suite.

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 139 A journey through history … t U1: benign tumor U2: malignant tumor O-#2 O-#1 R B p3: John Doe’s pathological formation p4p5 IUI-#3 #5 The government decides that malignancy doesn’t exist anymore: a convenient way to save on reimbursement and law suites.

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 140 Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

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 141 Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

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 142 Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

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 143 Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

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 144 Typology of expressions included in and excluded from a representation in light of relevance and relation to external reality

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 145 Valid presence Valid absence

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 Unjustified presence Unjustified absence

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 Conclusion

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 Summary of current deficiencies in traditional and formal terminologies (1) Terms often require “reading in context” Agrammatical constructions (paper-based indexing) Semantic drift as one moves between hierarchies Not (yet) useful for natural language understanding by software (but were not designed for that purpose)

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 Summary of current deficiencies in traditional and formal terminologies (2) labels for terms do not correspond with formal meaning underspecification (leading to erroneous classification in DL-based systems) overspecification (leading to wrong assumptions with respect to instances)

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 Axiom Concept-based terminology (and standardisation thereof) is there as a mechanism to improve understanding of messages by humans. It is NOT the right device –to explain why reality is what it is, how it is organised, etc., (although it is needed to allow communication), –to reason about reality, –to make machines understand what is real, –to integrate across different views, languages, conceptualisations,...

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 Why not ? Does not take care of universals and particulars appropriately Concepts not necessarily correspond to something that (will) exist(ed) –Sorcerer, unicorn, leprechaun,... Definitions set the conditions under which terms may be used, and may not be abused as conditions an entity must satisfy to be what it is Language can make strings of words look as if it were terms –“Middle lobe of left lung”...

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 Link to ontologies (1) Building high quality ontologies is hard. Not everybody has the right skills –Experts in driving cars are not necessarily experts in car mechanics (and the other way round). Ontologies should represent the state of the art in a domain, i.e. the science. –Science is not a matter of consensus or democracy (cfr HL7 RIM problem). Natural language relates more to how humans talk about reality or perceive it, than to how reality is structured. No high quality ontology without the involvement of ontologists.

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 Link to ontologies (2) Realist ontology is a powerful QA tool for building high quality ontologies AND high quality databases; Referent tracking, based on realist ontology, is a means to remove the ambiguity in data that cannot be solved by realist ontology alone; –It is a form of “adult” annotation Application of RT requires a globally accessible repository –Adds another level to interoperability. The use of “meaningless” IUIs allows very strict safety and security measures to be implemented.

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 Did we cover our dimensions for “better information”? Coverage Authority Objectivity Accuracy Timeliness Utility Understandability Realism-based Ontology Referent Tracking EHR Archetypes Referent-based Change management

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 Goal: new form of Evidence Based Medicine Now: –Decisions based on (motivated/justified by) the outcomes of (reproducable) results of well-designed studies Guidelines and protocols –Evidence is hard to get, takes time to accumulate. Future: –Each discovered fact or expressed belief should instantly become available as contributing to ‘evidence’, wherever its description is generated. –Data ‘eternally’ reusable independent of the purpose for which they have been generated.