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Introduction to Formal Ontology in Bio-medicine

This presentation provides an introduction to formal ontology in the field of bio-medicine, focusing on the use of ontologies in data and information organization and its relevance to healthcare. It covers topics such as ontological realism, Basic Formal Ontology (BFO), and biomedical applications based on BFO.

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Introduction to Formal Ontology in Bio-medicine

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  1. MHI501 – Introduction to Health InformaticsAn Introduction toFormal Ontology in Bio-medicineSUNY at Buffalo - November 18, 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. Overview • Data and information • Ontology (and relation to terminology) • Ontological Realism: a specific methodology for doing ontological analyses • Basic Formal Ontology (BFO): an upper level ontology based on Ontological Realism • Biomedical Applications based on BFO: • the Open Biomedical Ontologies Foundry • the Ontology of General Medical Science • adverse event management in the ReMINE project • Ontological Realism and (clinical) studies

  3. Data and Information

  4. Better care Better information A general belief:

  5. Being better informed Better care Better information ‘Information’ versus ‘informing’

  6. A general belief: Being better informed • Concerns primarily the delivery ofinformation, independent of the quality of the information: Being better informed Better Better care information

  7. pretty well covered long way to go 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.

  8. data organization model development further R&D (instrument and study optimization) add verify use Δ= outcome Generic beliefs application Today’s data generation and use observation & measurement

  9. data organization observation & measurement diagnosis add verify use Δ= outcome Generic beliefs treatment Example 1: clinician

  10. data organization hypothesis further R&D (instrument and study optimization) add verify use Δ= outcome Generic beliefs Example 2: researcher observation & measurement

  11. data organization model development further R&D (instrument and study optimization) add verify use Δ= outcome Generic beliefs application Example 3: device manufacturer / supplier observation & measurement

  12. Alarm signals: is biomedical science on the right track? • Why most published research findings are false. Ioannidis JPA (2005). PLoS Med 2(8): e124. • Institute for Clinical Research and Health Policy Studies, Department of Medicine, Tufts-New England Medical Center, Tufts University School of Medicine, Boston, Massachusetts. • Why Current Publication Practices May Distort Science. Young NS, Ioannidis JPA, Al-Ubaydli O (2008, October 7) PLoS Med 5(10): e201. doi:10.1371/journal.pmed.0050201. • Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland,

  13. Some (well-documented) claims • A research finding is less likely to be true when • the studies conducted in a field are smaller; • effect sizes are smaller; • there is a greater number and lesser preselection of tested relationships; • there is greater flexibility in designs, definitions, outcomes, and analytical modes; • there is greater financial and other interest and prejudice; • more teams are involved in a scientific field in chase of statistical significance. • Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. • For many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. Ioannidis JPA (2005) Why most published research findings are false. PLoS Med 2(8): e124.

  14. Patient-specific information Scientific “knowledge” Quality Information must cover … • EHR-EMR-ENR-… • PHR • Various modality related databases • Lab, imaging, … • Textbooks • Classification systems • Terminologies • Ontologies 1 3 2

  15. Means to structure the available information Key question: on what should the structure be based ?

  16. What is the structure based on ? (1) • Classification systems: on ‘properties’ of patients which are of importance for the purposes the system has been designed http://www.who.int/classifications/en/

  17. What is the structure based on ? (2) • Terminologies: • on ‘concepts’ • But terminologists fail to give a good answer on what a concept is

  18. What is the structure based on ? (3) • ‘Ontologies’ (mainstream view): • on ‘concepts’ • when designed by terminologists • on ‘classes’ • when designed by software engineers and computer scientists • a class is a construct that is used as a blueprint to create objects of that class.? • a class is a cohesive package that consists of a particular kind of metadata. ?? • a class usually represents a noun, such as a person??? http://en.wikipedia.org/wiki/Class_(computer_science)

  19. What should the structure be based on ? on the structure of Reality !!! (yes, I’m shouting)

  20. Ontology

  21. ‘Ontology’ • In philosophy: • Ontology(no plural) is the study of what entities exist and how they relate to each other; • In computer science and many biomedical informatics applications: • An ontology(plural: ontologies) is a shared and agreed upon conceptualization of a domain; • The realist view within the Ontology Research Group combines the two: • We use Ontological Realism, a specific methodology that uses ontology as the basis for building high quality ontologies, using reality as benchmark.

  22. Realism Conceptualism Nominalism Universal Concept Collection of particulars yes: in particulars perhaps: in minds no Three major views on reality • Basic questions: • What does a general term such as ‘diabetes’ refer to? • Do generic things exist?

  23. Realism Conceptualism Nominalism Universal Concept Collection of particulars yes: in particulars perhaps: in minds no Dominant view in computer science is conceptualism • Basic questions: • What does a general term such as ‘tree’ refer to? • Do generic things exist?

  24. concept Embedded in Terminology Semantic Triangle object term Dominant view in computer science is conceptualism Realism Conceptualism Nominalism

  25. Terminological versus Ontological approach • The terminologist defines: • ‘a clinical drug is a pharmaceutical product given to (or taken by) a patient with a therapeutic or diagnostic intent’. (RxNorm) • The ontologist thinks: • Does ‘given’ includes ‘prescribed’? • Is manufactured with the intent to … not sufficient? • Are newly marketed products – available in the pharmacy, but not yet prescribed – not clinical drugs? • Are products stolen from a pharmacy not clinical drugs? • What about such products taken by persons that are not patients? • e.g. children mistaking tablets for candies.

  26. Why is this important ? • Not as much for humans: • Our ‘minds’ are very good in resolving ambiguities, or fill in gaps, even at ‘unconscious’ levels. • But for machines (computers, software): • They can’t deal with imprecise, vague or ambiguous statements.

  27. Representation and Reference terms concepts about First Order Reality The semantic triangle revisited concepts objects terms

  28. representational units universals particulars Terminology Realist Ontology Representation and Reference terms concepts about objects First Order Reality

  29. Terminology Realist Ontology Representation and Reference terms concepts representational units about objects universals particulars First Order Reality

  30. Terminology Realist Ontology Representation and Reference representational units terms concepts cognitive units communicative units about objects universals particulars First Order Reality

  31. Representational units in various • forms about (1), (2) or (3) (2) Cognitive entities which are our beliefs about (1) (1) Entities with objective existence which are not about anything Three levels of reality in Realist Ontology Terminology Realist Ontology Representation and Reference representational units cognitive units communicative units universals particulars First Order Reality

  32. Ontological Realism

  33. No serious scholar should work with ‘concepts’

  34. Slow penetration of the idea …

  35. what is a concept description a description of? More serious scholars become convinced …

  36. but Kantians will never …

  37. The basis of Ontological Realism • There is an external reality which is ‘objectively’ the way it is; • That reality is accessible to us; • We build in our brains cognitive representations of reality; • We communicate with others about what is there, and what we believe there is there. 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

  38. Ontological Realism makes three crucial distinctions • Between data and what data are about; • Between continuants and occurrents; • Between what is generic and what is specific. Smith B, Ceusters W. Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies. Applied Ontology, 2010. (forthcoming)

  39. Ontological Realism makes crucial distinctions • Between data and what data are about: • Level 1 entities (L1): • everything what exists or existed • some are referents (‘are’ used informally) • some are L2, some are L3, none are L2 and L3 • Level 2 entities (L2): beliefs • all are L1 • some are about other L1-entities but none about themselves • Level 3 entities (L3): expressions • all are L1, none are L2 • some are about other L1-entities and some about themselves

  40. L3 L2 L1

  41. L3 L2 L1 OBO Foundry

  42. Ontological Realism makes crucial distinctions • Between data and what data are about; • Between continuants and occurrents: • obvious differences: • a person versus his life • a disease versus its course • space versus time • more subtle differences (inexistent for flawed models e.g. HL7-RIM): • observation (data-element) versus observing • diagnosis versus making a diagnosis • message versus transmitting a message

  43. ‘person’ ‘drug’ ‘insulin’ ‘W. Ceusters’ ‘my sugar’ DIAGNOSIS my doctor’s work plan my doctor’s diagnosis INDICATION my doctor’s computer my doctor PATHOLOGICAL STRUCTURE PERSON me my NIDDM DISEASE DRUG my blood glucose level PORTION OF INSULIN MOLECULE Basic Formal Ontology Referent Tracking Between ‘generic’ and ‘specific’ Generic Generic Specific Specific L3. Representation L2. Beliefs (knowledge) L1. First-order reality

  44. Observations and similarities

  45. Observations and similarities Are these pictures of concepts or of horses ? Is this a sensible question: ‘What concepts have tails and do …?’

  46. Observations and similarities Are these pictures of concepts? If concepts are in brains, that must be awfully big brains! Are these pictures of anything at all?

  47. Basic Formal Ontology:an upper ontology based onOntological Realism

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