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 SNOMED and the UMLS: an introduction to biomedical terminology SUNY at Buffalo - November 4, 2010 Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences Ontology Research Group 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 What is going on here ? ah sh uqwu ja ? ywye has uw has!

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 L1 L2 L3 What is going on here ? ah sh uqwu ja ? ywye has uw has!

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 There is an external reality which is ‘objectively’ the way it is, and That reality is accessible to us; –  L1 We build in our brains cognitive representations of reality; –  L2 We communicate with others about what is there, and what we believe there is there. –  L3 Basic axioms 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

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 Language is ambiguous ‘I know that you believe that you understood what you think I said, but I am not sure you realize that what you heard is not what I meant.’ –Robert McCloskey, State Department spokesman (attributed). http://www.quotationspage.com/quotes/Robert_McCloskey/

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 Language is ambiguous Often we can figure it out … in Miami hotel lobby warning on plastic bag

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 Language is ambiguous in Amsterdam hotel elevator Sometimes, we can not …

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U A double mystery (It is argued that) On September 9th, 1935, Carl Austin Weiss shot Senator Huey Long in the Louisiana State Capitol with a.35 calibre pistol. Long died from this wound thirty hours later on September 10th. Weiss, on the other hand, received between thirty-two and sixty.44 and.45 calibre hollow point bullets from Long's agitated bodyguards and died immediately. Sorensen, R., 1985, "Self-Deception and Scattered Events", Mind, 94: 64-69. Questions: –Did Weiss kill Senator Long ? –If so, when did he kill him ?

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 Relevance to Healthcare? ‘Nuances in the English language can be both challenging and amusing, however, when variants in language impact treatment, safety and billing, it is all challenge and no humor. Although English contains a reasonable degree of conformity, divergence in phrasing and meaning can compound comprehension problems and impact patient safety. These language "woes" can be minimized through the use of sophisticated healthcare IT systems...’ Schwend GT. The language of healthcare. Variance in the English language is harming patients and hospitals' bottom lines. Is healthcare IT the solution? Health Manag Technol. 2008 Feb;29(2):14, 16, 18

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 Indeed, these days, not just people communicate …

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 Understanding content (1) “John Doe has a pyogenic granuloma of the left thumb” We see:     The machine sees:

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 Understanding content (2) John Doe pyogenic granuloma of the left thumb The XML misunderstanding We see:    The machine sees:

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 The clever (?) business man and his XML card John Nitwit 524 Moon base avenue Utopia … Is this the name of the business card or of the business card owner?

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 ({valgising}5(osteotomy)1{[of]3(humerus)2}4)22 ((cutting)21 {[TO_ACHIEVE]6((Deed:valgising)7 {[ACTS_ON]17(Pathology:pathologicalposture)18}19)20}5 {[ACTS_ON]3(Anatomy:humerus)2}4)22 A better example

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 Intermediate conclusion We need for sure methods and techniques that allow: –people to express exactly what they mean, –people to understand exactly what is communicated to them, –machines to communicate information without any distortion. If information overload is a problem, we also need methods and techniques that allow machines to understand exactly what is communicated to them.

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 Relevance to Healthcare? ‘Nuances in the English language can be both challenging and amusing, however, when variants in language impact treatment, safety and billing, it is all challenge and no humor. Although English contains a reasonable degree of conformity, divergence in phrasing and meaning can compound comprehension problems and impact patient safety. These language "woes" can be minimized through the use of sophisticated healthcare IT systems with terminology management services.’ Schwend GT. The language of healthcare. Variance in the English language is harming patients and hospitals' bottom lines. Is healthcare IT the solution? Health Manag Technol. 2008 Feb;29(2):14, 16, 18

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 ah sh uqwu ja ? ywye has uw has! OBO Foundry

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 Terminology / Ontology divide Terminology: –solves certain issues related to language use, i.e. with respect to how we talk about entities in reality (if any); Relations between terms / concepts –does not provide an adequate means to represent independent of use what we talk about, i.e. how reality is structured; Women, Fire and Dangerous Things (Lakoff). Ontology (of the right sort) : –Language and perception neutral view on reality. Relations between entities in first-order reality This is the ‘terminology / ontology divide’

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 Terminology (is) for dummies

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 But what the word ‘concept’ denotes, is usually not clarified and users of it often refer to different entities in a haphazard way: 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

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 SNOMED about diseases and concepts (until 2010) ‘Disorders are concepts in which there is an explicit or implicit pathological process causing a state of disease which tends to exist for a significant length of time under ordinary circumstances.’ And also: “Concepts are unique units of thought”. Thus: Disorders are unique units of thoughts in which there is a pathological process …??? And thus: to eradicate all diseases in the world at once we simply should stop thinking ?

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 SNOMED now (since version 201007) Concept: An ambiguous term. Depending on the context, it may refer to: 1.A clinical idea to which a unique ConceptId has been assigned. 2.The ConceptId itself, which is the key of the Concepts Table (in this case it is less ambiguous to use the term ‘concept code’). 3.The real-world referent(s) of the ConceptId, that is, the class of entities in reality which the ConceptId represents.

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 But what the word ‘concept’ denotes, is usually not 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

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 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.

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 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

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

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

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

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 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 referent can have different senses 2 names with the same sense have the same referent (synonyms) a name with a sense does not need to have a referent (“Beethoven’s 10 th symphony”) referent sense name

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 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.

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

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 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

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 MeSH: Medical Subject Headings

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 MeSH

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 Use of MeSH in PUBMED Wolfram syndrome

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 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

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 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]

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 Is this a good idea ? Cover subject matter of papers Cover the form of papers

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 Principle A representation should not mix object language and meta language –object language describes the referents in the subject domain –meta language describes the object language

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 40 Geographic Locations: a good hierarchy ? Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + mereological mess mixture of geographic entities with socio- political entities mixture of space and time

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 Geographic Locations [Z01] Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + Ancient Lands [Z01.586.035] + Austria-Hungary [Z01.586.117] Commonwealth of Independent States [Z01.586.200] + Czechoslovakia [Z01.586.250] + European Union [Z01.586.300] Germany [Z01.586.315] + Korea [Z01.586.407] Middle East [Z01.586.500] + New Guinea [Z01.586.650] Ottoman Empire [Z01.586.687] Prussia [Z01.586.725] Russia (Pre-1917) [Z01.586.800] USSR [Z01.586.950] + Yugoslavia [Z01.586.980] +

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 42 Geographic Locations [Z01] Africa [Z01.058] + Americas [Z01.107] + Antarctic Regions [Z01.158] Arctic Regions [Z01.208] Asia [Z01.252] + Atlantic Islands [Z01.295] + Australia [Z01.338] + Cities [Z01.433] + Europe [Z01.542] + Historical Geographic Locations [Z01.586] + Indian Ocean Islands [Z01.600] + Oceania [Z01.678] + Oceans and Seas [Z01.756] + Pacific Islands [Z01.782] + Ancient Lands [Z01.586.035] + Austria-Hungary [Z01.586.117] Commonwealth of Independent States [Z01.586.200] + Czechoslovakia [Z01.586.250] + European Union [Z01.586.300] Germany [Z01.586.315] + Korea [Z01.586.407] Middle East [Z01.586.500] + New Guinea [Z01.586.650] Ottoman Empire [Z01.586.687] Prussia [Z01.586.725] Russia (Pre-1917) [Z01.586.800] USSR [Z01.586.950] + Yugoslavia [Z01.586.980] +

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 Principle A hierarchical structure should not represent distinct hierarchical relations unless they are formally characterized

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 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

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 Diabetes Mellitus in MeSH 2008 ? Different set of more specific terms when different path from the top is taken. 2

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: 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

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 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

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 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”

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 What would it mean for a particular disease? 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 ?

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

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 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

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 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 ?

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 SNOMED: Systematized Nomenclature of Medicine

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 SNOMED’s origin: SNOP from CAP (1965)

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 Since mid 2007

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 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

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 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

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 58 Evolution of SNOMED descriptions

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 Term additions and deletions since 20020731

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 Number of changes over time (2007) 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

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

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 : 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

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 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:

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 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 ?

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

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 Find the problem

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 Find the problem new-1 new-2

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

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

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 terms

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 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 ?

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

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 Find the problem

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

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

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 Uncorrected lexical mapping

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

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 SNOMED-CT: abundance of false synonymy nose bones fracture

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

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 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 = =

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

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” 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 ?

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 Find the problem

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 Evolution in SNOMED-CT

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 Historical changes in SNOMED

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

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

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 UMLS: Unified Medical Language System

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 93 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

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

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 Linking of concept-based resources Declaring equivalence (synonymy) between concepts from distinct resources comes down to: –stating that these concepts mean the same thing, –however not what they mean. There is no common underlying ontology: –Trying to decipher what is meant by concept structures from separate resources is hampered by inconsistencies between these resources. Use of an ontology language such as OWL might reveal the inconsistencies, but not resolve them.

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 UMLS Semantic Network

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 Relationships Is_a physically related to spatially related to temporally related to functionally related to conceptually related to

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 “Biologic Function” 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 Semantic Network "affects" Hierarchy

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 100 Metathesaurus: merging terminologies cycles in hierarchical relationships

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 Conclusion

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 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)

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 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)

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 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,...

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 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”...

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 Link to ontologies 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.