The Ontologically Privileged Status of the Past

1. The Ontologically Privileged Status of the Past Barry Smith

2. Universals vs. instances • Assertions in scientific texts pertain to universals in reality • Assertions in a lab report pertain (also) to instances of these universals

3. Universals are those invariants in reality • which make possible • the use of general terms in scientific inquiry • the use of standardized therapies in clinical care • the use of standardized procedures in business transactions • ...

5. relate indiscriminately to past, present and future

6. relate only to the past

9. UMLS Semantic Network

10. Organ Part Organ Subdivision Anatomical Space Anatomical Structure Organ Cavity Subdivision Organ Cavity Organ Organ Component Serous Sac Tissue Serous Sac Cavity Subdivision Serous Sac Cavity Pleural Sac Pleura(Wall of Sac) Pleural Cavity Parietal Pleura Visceral Pleura Interlobar recess Mediastinal Pleura Mesothelium of Pleura Foundational Model of Anatomy

11. Organ Part Organ Subdivision Anatomical Space Anatomical Structure Organ Cavity Subdivision Organ Cavity Organ Organ Component Serous Sac Tissue Serous Sac Cavity Subdivision Serous Sac Cavity is_a Pleural Sac Pleura(Wall of Sac) Pleural Cavity part_of Parietal Pleura Visceral Pleura Interlobar recess Mediastinal Pleura Mesothelium of Pleura

12. Gene • Ontology

13. Gene • Ontology

14. Holy grail of biomedical informatics = integration of genomic and EHR data Main obstacles 1. Poor facility for dealing with time and instances / particulars in current ontologies 2. Poor facility for dealing with instances / particulars in current clinical record systems

15. Current ontologies are about meanings (‘concepts’, ‘conceptualizations’)

16. The Ontologically Privileged Status of Universals

17. The Ontologically Privileged Status of Universals (a.k.a. Concepts)

18. The concept diabetes mellitus becomes ‘associated with a diabetic patient’ • concept patient concept diabetes • what it is on the • side of the patient ? ?

19. What is the relation here? Not a relation between concepts The concept diabetes mellitus becomes ‘associated with a diabetic patient’ • concept patient concept diabetes • what it is on the • side of the patient ? ?

20. A is_a B =def. ‘A’ is more specific in meaning than ‘B’ • A contains B =def • the concept A stands in a containment relation to the concept B • A causes B =def • the concept A stands in a causative relation to the concept B

21. GALEN • vomitus contains carrot

22. UMLS Semantic Network: • Food causes Experimental Model of Disease • Biomedical or Dental Material causes Mental or Behavioral Dysfunction • Manufactured Object causesDisease or Syndrome

23. vomitus contains carrot • The authors of ontologies have not paid attention to the question whether these are all or some assertions

24. because they have not paid attention to instances • some instances of vomitus contain instances of carrot • all instances of vomitus contain instances of carrot

25. IFOMIS proposal: move from associative relations between concepts/meanings to strictly defined relations between the universals (types, kinds) in realityembraced also by Gene Ontology Consortium

26. Key idea • Ontological relations like • contains, part_of, causes • are relations between universals, • but to define them properly we need to take account of instances and of time

27. Three kinds of relations • <universal, universal>: is_a, part_of, ... • <instance, universal>: this throb here and now instance_of the class throb • <instance, instance>: Mary’s heart part_of Mary at t

28. part_of • A part_of B =def. • given any particular a and any time t, • if a is an instanceof A at t, • then there is some instance b of B • such that • a is an instance-level part_ofb at t • HAS ALL-SOME FORM

29. same instance C1 C c att c att1 time transformation_of mature RNA transformation_of pre-RNAadult transformation_of child

30. transformation_of • A transformation_of B =def • for all a, t, if a is an instance of A at t then there is some t´ earlier than t which is such that a is an instance of B at t´ HAS ALL-SOME FORM

31. transformation_of • in short: • A transformation_of B =def. any instance of Awas at some earlier time an instance of B • Contrast: • A transforms_into B • child transforms_into adult • The ontologically privileged status of relations pointing towards the past

32. C1 C c att c att1 embryological development

33. tumor development C1 C c att c att1

34. Advantages of the methodology of enforcing commonly accepted coherent definitions • promote quality assurance (better coding) • guarantee automatic reasoning across ontologies and across data at different granularities • yields direct connection to times and instances in EHR

35. The story of Jane Smith(with thanks to Werner Ceusters)

36. Jane’s favourite supermarket The freezer section of Jane’s favourite supermarket The only available warning sign used outside A very suspiciously shaped upper leg July 4th, 1990: Jane goes shopping:

37. A visit to the hospital • City Health Centre Dr. Peters • (City HC) Dr. Longley

38. Diagnosis: a severe spiral fracture of the femur

39. The City HC’s medical record captures in a structured form all of the ‘clinicallysignificant’ information in the narrative notes • Rector AL, Nowlan WA, Kay S, Goble CA, Howkins TJ. • A framework for modelling the electronic medical record. • Methods Inf Med. 1993 Apr;32(2):109-19.

40. Dr. Peters 04/07/1990 – 17:10 Orthopedics Jane Smith Emergency visit: 04/07/1990 – 17.00 Severe Left upper leg Since fall on floor Constant 26442006 closed fracture of shaft of femur 81134009 fracture, closed, spiral www.medappz.com/ Structured Medical Record

41. CityHC’s representation formalismfor statements in records Occurrences: “are specific occurrences of individuals and must be situated in space and time. The most important group of occurrences are observations — i.e. agents’ observations of individuals.”

42. City HC’s EHR model

43. Rector et al: • “Every occurrence level statement concerning the Jane Smith’s Fracture of the Femuris an observation of the corresponding individual.” • “The existence of the individual JaneSmith’s Fracture of Femur does not imply that Jane Smith has, or has ever had, a fracture ofthe femur, but merely that some observation has been made about Jane Smith regarding afracture of the femur.” • “(The only observation recorded about Jane Smith’s Fracture of theFemur might be that she did not have it.)”

44. Different patients. Same supermarket? Maybe the same (irrelevant ?) freezer section ? Or different supermarkets, but always in the freezer sections ? PtID Date ObsCode Narrative Same patient, same hypertension code: Same (numerically identical) hypertension ? 5572 298 5572 5572 2309 5572 5572 47804 298 5572 5572 21/03/1992 04/07/1990 17/05/1993 12/07/1990 01/04/1997 22/08/1993 01/04/1997 04/07/1990 22/08/1993 12/07/1990 03/04/1993 9001224 9001224 26442006 81134009 79001 26442006 9001224 26442006 2909872 79001 58298795 Accident in public building (supermarket) Essential hypertension closed fracture of shaft of femur Fracture, closed, spiral closed fracture of shaft of femur closed fracture of shaft of femur Other lesion on other specified region Closed fracture of radial head Essential hypertension Accident in public building (supermarket) Accident in public building (supermarket) 5572 04/07/1990 79001 Essential hypertension 0939 24/12/1991 255174002 benign polyp of biliary tract Same patient, different dates, same fracture codes: same (numerically identical) fracture ? 2309 21/03/1992 26442006 closed fracture of shaft of femur Same patient, same date, 2 different fracture codes: same (numerically identical) fracture ? Same patient, different dates, Different codes. Same (numerically identical) polyp ? Different patients, same fracture codes: Same (numerically identical) fracture ? 0939 20/12/1998 255087006 malignant polyp of biliary tract Problems

45. Main problems of EHRs • Statements refer only implicitly to the concrete entities about which they give information. • Codes are general: they tell us only that some instance of the class the codes refer to, is referred to in the statement, but not what instance precisely. • Mixing up the act of observation and the thing observed. • Mixing up statements and the entities these statements refer to.

46. Consequences • 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), HIV contaminated blood from donors, food from unhygienic source, ... • Hampers prevention

47. Proposed solution:Referent Tracking • Purpose: • explicitreference to the concrete individual entities relevant to the accurate description of each patient’s condition, therapies, outcomes, ... • Method: • Introduce an Instance Unique Identifier(IUI) for each relevant particular / instance

48. CUI (coo-ey): Concept Unique Identifier (e.g. a SNOMED code) • UUI (oo-ey): Universal Unique Identifier • IUI (you-ey): Instance Unique Identifier (e.g. a Social Security Number)

49. Referent tracking • a response to the hard NLP problem of reference resolution in running text

50. Ontology • An ontology is a representation of some pre-existing domain of realitywhich • (1) reflects the properties of the objects within its domain in such a waythat there obtains a systematic correlation between realityand the representation itself, • (2) is intelligible to a domain expert • (3) is formalized in a way that allows it to support automatic information processing