1. 1 Scale and Context: Issues in Ontologies to link Health- and Bio-Informatics Scale and Context: Issues in Ontologies to link Health- and Bio-Informatics Alan Rector, Jeremy Rogers, Angus Roberts, Chris Wroe Bio and Health Informatics Forum/ Medical Informatics Group Department of Computer Science, University of Manchester rector@cs.man.ac.uk www.cs.man.ac.uk/mig img.man.ac.uk www.clinical-escience.org www.opengalen.org

2. 2 Organisation of Talk Informal presentation, motivation & examples Intro to logic based ontologies How to use logic based ontologies to represent scales and context –Making context modular – normalisation –Recurrent distinctions and tests for those distinctions Making logic based ontologies usable –Views and Intermediate Representations Summary

3. 3 Example Problems of Context Classification by multiple axes –e.g. Molecular action, physiologic, and pathological effects Chloride transport & Cystic fibrosis Biological Scope –eg. Normal/Abnormal, Human/Mouse Conceptual view –e.g. the Digital Anatomist Foundational Model of organs vs Clinical convention – Is the pericardium a part of the heart?

4. 4 Basic Approach Separate information into independent modules –Normalise the ontology “The truth, the whole truth, and nothing but the truth” Add explicit contextual information –Don’t distort the structure Add context to it explicitly

5. 5 Why use Logic-based Ontologies? because Knowledge is Fractal! & Requirements are Diverse Coherence without Uniformity!

6. 6 Logic-based Ontologies: Conceptual Lego hand extremity body acute chronic abnormal normal ischaemic deletion bacterial polymorphism cell protein gene infection inflammation Lung expression

7. 7 Logic-based Ontologies: Conceptual Lego “ SNPolymorphism of CFTRGene causing Defect in MembraneTransport of ChlorideIon causing Increase in Viscosity of Mucus in CysticFibrosis …” “Hand which is anatomically normal”

8. 8 Logic based ontologies A formalisation of semantic nets, frame systems, and object hierarchies via KL-ONE and KRL “is-kind-of” = “implies” (“logical subsumption”) –“Dog is a kind of wolf” means “All dogs are wolves” Modern examples: DAML+OIL /“OWL”?) Older variants LOOM, CLASSIC, BACK, GRAIL, K-REP, …

9. 9 Encrustation + involves: MitralValve Thing + feature: pathological Structure + feature: pathological + involves: Heart Logic Based Ontologies: The basics Thing Structure HeartMitralValveEncrustation MitralValve * ALWAYS partOf: Heart Encrustation * ALWAYS feature: pathological Feature pathological red + (feature: pathological) red + partOf: Heart red + partOf: Heart PrimitivesDescriptionsDefinitionsReasoning Validating (constraining cross products)

10. 10 Bridging Bio and Health Informatics Define concepts with ‘pieces’ from different scales and disciplines and then combine them –“Polymorphism which causes defect which causes disease” Use concepts which make context explicit –“ ‘Hand which is anatomically normal’  has five fingers” “ ‘ Normal human prostate’  has three lobes” Use different subproperties for different contexts –“Abnormalities of clinical parts of the heart”

11. 11 Bridging Scales with Ontologies Genes Species Protein Function Disease Protein coded by (CFTRgene & in humans) Membrane transport mediated by (Protein coded by (CFTRgene in humans)) Disease caused by (abnormality in (Membrane transport mediated by (Protein coded by (CTFR gene & in humans)))) CFTRGene in humans

12. 12 Use composition to express context Normal and abnormal Hand  isSubdivisionOf some UpperExtremity Hand & AnatomicallyNormal  hasSubdivision exactly-5 fingers Homologies and Orthologies Thumb of Hand of Human  hasFeature Opposable Thumb of Hand of NonHumanPrimate  ¬ hasFeature Opposable

13. 13 More detailed example Body Prostate some Body mammal Body mammal male Body human male Body mouse male =5 Prostate P1P2P3P4P5 Prostate =3 Lobe L1L2L3 =1

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15. 15 Represent context and views by variant properties Organ Heart Pericardium OrganPart CardiacValve Disease of part_of Heart Disease of Pericardium is_part_of is_structurally_part_of is_clinically_part_ of

16. 16 What we want to avoid: combinatorial explosions The “Exploding Bicycle” From “phrase book” to “dictionary + grammar” –1980 - ICD-9 (E826) 8 –1990 - READ-2 (T30..) 81 –1995 - READ-3 87 –1996 - ICD-10 (V10-19 Australian) 587 V31.22 Occupant of three-wheeled motor vehicle injured in collision with pedal cycle, person on outside of vehicle, nontraffic accident, while working for income –and meanwhile elsewhere in ICD-10 W65.40 Drowning and submersion while in bath-tub, street and highway, while engaged in sports activity X35.44 Victim of volcanic eruption, street and highway, while resting, sleeping, eating or engaging in other vital activities

17. 17 The Cost 1: Normalising (untangling) Ontologies Structure Function Part-whole Structure Function Part-whole

18. 18 The Cost 1: Normalising (untangling) Ontologies Making each meaning explicit and separate PhysSubstance Protein ProteinHormone Insulin Enzyme Steroid SteroidHormone Hormone ProteinHormone^ Insulin^ SteroidHormone^ Catalyst Enzyme^ Hormone = Substance & playsRole-HormoneRole ProteinHormone = Protein & playsRole-HormoneRole SteroidHormone = Steroid & playsRole-HormoneRole Catalyst =Substance & playsRole CatalystRole Insulin  playsRole HormoneRole …build it all by combining simple trees Enzyme ?=? Protein & playsRole-CatalystRole PhysSubstance Protein ‘ ProteinHormone’ Insulin ‘Enzyme’ Steroid ‘SteroidHormone’ ‘Hormone’ ‘ProteinHormone’ Insulin^ ‘SteroidHormone’ ‘Catalyst’ ‘Enzyme’ … ActionRole PhysiologicRole HormoneRole CatalystRole … … Substance BodySubstance Protein Insulin Steroid …

19. 19 Normalisation Building ontologies from orthogonal trees Each tree is homogeneous and based on subsumption –One prinicple – one of function, structure, cause,… Every primitive has exactly 1 primitive parent –All multiple classification done by the logic All self-standing primitives disjoint

20. 20 The Cost: 2 – Clean Distinctions & Tests Repeating patterns within levels –Structures vs Substances –Flavours of part-whole –Part-whole vs containment, connection, branching –Process/Event vs Thing (“Endurant” vs “Perdurant”) –… Repeating patterns across levels –Multiples at one level act as substances at the next –Substances span levels; structures are specific to a level

21. 21 Repeating Patterns within each level Structures vs Substances (Discrete vs Mass) –Structures are made of substances Organs are made of tissue –Parts & portions Structures have parts & subdivisions,… Substances have portions –Portions can have proportions & concentrations

22. 22 Tests Structures (Discrete) –Can you count it? Is one part different from another? Is it made of something(s)? Books, organs, ideas, individual cells, organisations, … Substance (Mass) –Are all bits the same? Can something be made of it? Can you talk about “A piece of it”? “A lump of it”? “A stream of it”? … Water, sodium, tissue, blood, …

23. 23 Repeating Patterns within each level Part-whole vs containment –Parthood is organisational The wall is part of the cell; The cornea is part of the eye –Containment is physical The inclusion is contained in the cell The marrow is contained in the bone –Often occur together Nucleus is a part of and contained in the cell The retina is part of and contained in the eye

24. 24 Tests Parts –If I take the part away, is the whole incomplete? –If the part is damaged is the whole damaged? –If I do something to the part do I do something to the whole? Containment –Is the contained thing inside the container? –Is the relationship spatial/physical? (or temporal?)

25. 25 Repeating Patterns bridging levels Multiples of structures at one level behave as substances at the next –“Blood is made of in part a multiple of red cells” “Tissue is made of in part a multiple of cells” “A rash is a multiple of spots” “Polyposis is a multiple of polyps” “A flock is a multiple of birds” Multiples are not Sets –Not defined by members Membership can change (intensional rather than extensional) –Action on the singleton is not action on the multiple; Action on the whole is (usually) action on the singletons If I treat a spot, I do not treat the rash If I treat the rash, I treat the spots

26. 26 Tests Multiples –Name for the singleton – “grain”, “cell”, “bird”? –Singletons are countable? –Multiple is measurable rather than countable? –Odd to say part-of “This cell is part of the Arm”?

27. 27 But make it simple Intermediate representations and views –OWL + Detailed Schema is the Assembler Language FaCT/SHIQ/… is the machine code Almost no one writes in assembler –let alone machine code Separate “terms” and “concepts” –Language/labels from concepts

28. 28 Tools Versioning Language Metadata Provenance Intermed Rep Links to Resources Layered Architecture Layered Architecture Indexed KB (Frame Like) DL Protégé + “OilEd-II”+ …? Protégé + “OilEd-II”+ …?

29. 29 Example: An Intermediate Representation for Surgery "Open fixation of a fracture of the neck of the left femur" MAIN fixing ACTS_ON fracture HAS_LOCATION neck of long bone IS_PART_OF femur HAS_LATERALITY left HAS_APPROACH open

30. 30 The formal “assembler” version hasSpecificSubprocess (‘SurgicalAccessing’ hasSurgicalOpenClosedness (SurgicalOpenClosedness which hasAbsoluteState surgicallyOpen)) (‘SurgicalProcess’ which isMainlyCharacterisedBy (performance which isEnactmentOf (‘SurgicalFixing’ which actsSpecificallyOn (PathologicalBodyStructure which < involves Bone hasUniqueAssociatedProcess FracturingProcess hasSpecificLocation (Collum which isSpecificSolidDivisionOf (Femur which hasLeftRightSelector leftSelection))>))))

31. 31 Result Training time:3 mo  3 days + 3 days Productivity:25/day  100/day Central reconciliation: 50%+  10% Local cycle time:3 months  <1 week “Dependencies”High  Low Author satisfaction:Low  High Disputes:Frequent  Rare Repeatability:Low  High Even Pre Web!

32. 32 Navigation vs Retrieval/Reference “Access terminology” & “Reference terminology” Access follows model of use –e.g. MeSH, MEDCin Hierarchy is what is needed next “to hand” –People find easy; Software hard Retrieval follows model of meaning –Logic based ontologies Hierarchy means “is-kind-of” / subsumption –People may find odd; Software is easy Need Both - & visualisations of both –The logic based structure isn’t enough Views and intermediate representations

33. 33 What’s in a View/ Intermediate Representation? Explicit Context in Ontology “Assembler” User Oriented Structures Language semantic transformations & Filters linguistic generation & search

34. 34 Summary Let the logic engine do the work Logic based ontologies can bridge granularities & represent context explicitly –And manage the potential combinatorial explosions To do so –Views and Interface – usable, flexible & easy to learn Entry, Navigation, & Use are different –Structure – explicit & modular – “Normalised” –Conception – clean testable distinctions –Tools & Architecture - layered & comprehensive The logic is the assembly language

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36. 36 Some Healthcare Terminologies

37. 37 Some Healthcare Terminologies ICD 9/10 Traditional paper thesauri -CM versions essential for billing (and –AM) CPT – Clinical Procedure Terminology “Simple” list Clinical Terms (Read Codes) V2 Simple hierarchy Still dominant in UK general practice SNOMED-CT At least “logic assisted” Political questions… NCI Cancer Ontology “Logic based in parts” – work in progress

38. 38 Others Standards Related –Loinc – laboratory data –Increasingly structured – “logic assisted” aspirations –HL7 Vocabulary TC –Specialised vocabularies – Inspiration for OHT –Links to RxNorm –Snomed Dicom Microglossary (SDM) –Image related information – not related tNOMED Open Source –OpenGALEN Common Reference Model Logic based – multilingual – a resource rather than a terminology –Basis of UK Drug Ontology –Open Health Terminology Watch this space –Focusing on UMLS –Likely to be at least “logic assisted”

39. 39 Special Purpose Anatomy –Digital Anatomist Foundational Model of Anatomy FMA Principled frame based representation –Superb reference point for structural anatomy »Needs functional and clinical supplements –http://sig.biostr.washington.edu/projects/da/http://sig.biostr.washington.edu/projects/da/ Drugs –RxNorm and VA projects –See Steve Brown & Stuart Nelson –UK Primary Care Drug Dictionary UKCPRS (Secondary Care) Drug Ontology (OpenGALEN based) –MEDDRA, FDA, Proprietary, …, …, …

40. 40 Unified Medical Language System (UMLS) Common reference point and link to MeSH Terms and literature –De facto standard for universal identifiers Concept Unique Identifiers (CUIs) Lexical Unique Identifiers (LUIs) String Unique Identifiers (SUIs) –Valuable in itself: Huge resource for mining and restructuring Udo Hahn and Stefan Schulz “CoMMeT – Conceptual Model of Medical Terminology – http://www.coling.uni-freiburg.de/pub/schulz/commet/http://www.coling.uni-freiburg.de/pub/schulz/commet/ Alexa McCray is speaking next