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Towards an Ontological Treatment of Disease and Diagnosis

Towards an Ontological Treatment of Disease and Diagnosis. Barry Smith New York State Center of Excellence in Bioinformatics and Life Sciences University at Buffalo.

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Towards an Ontological Treatment of Disease and Diagnosis

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  1. Towards an Ontological Treatment of Disease and Diagnosis Barry Smith New York State Center of Excellence in Bioinformatics and Life Sciences University at Buffalo http://ontology.buffalo.edu/smith

  2. Anders Grimsmo, “Patients, diagnoses and processes in general practice in the Nordic countries. An attempt to make data from computerised medical records available for comparable statistics”Scandinavian Journal of Primary Health Care, 2001 • “The major obstacle to extracting more epidemiological data from computerised medical records is caused by information in the databases not being uniquely linked to episodes of care.” http://ontology.buffalo.edu/smith

  3. What is to be linked with what? What is information in the databases about? To answer this question (to assign numbers to discrete entities), we need a good ontology of the care domain, including episodes of care on the one hand and entities on the side of the patient on the other. http://ontology.buffalo.edu/smith

  4. and we need to take account of context – of multiple diseases – of the patient’s style of life – of the patient’s environment – of specific aspects of the presentation http://ontology.buffalo.edu/smith

  5. we do this by paying attention to natural language but the more we succeed in this, the more difficult it is to aggregate the data disease of UMLSitis http://ontology.buffalo.edu/smith

  6. OBO Foundry

  7. Buffalo Longitudinal Cancer Data Even with the best of intentions, and even if we just use one coding system, results are not always what they seem Problem of SNOMEDitis with acknowledgements to NLM: 1R21LM009824-01A1

  8. Why does SNOMED change so much?

  9. SNOMED CT: Anaplasmamarginale (organism) with acknowledgements to NLM: 1R21LM009824-01A1

  10. infectious agent is_a navigational concept with acknowledgements to Werner Ceusters NLM: 1R21LM009824-01A1

  11. infectious agent is_a navigational concept

  12. with acknowledgements to NLM: 1R21LM009824-01A1

  13. with acknowledgements to NLM: 1R21LM009824-01A1

  14. with acknowledgements to NLM: 1R21LM009824-01A1

  15. with acknowledgements to NLM: 1R21LM009824-01A1

  16. Why does SNOMED change so much? Problems with ‘concept’  no real coherence as to what SNOMED is representing

  17. Why does SNOMED change so much? No proper hierarchy (of more and less general) Confusion of disorders (continuants) with etiological and diagnostic processes (occurrents) and of both with information entities (‘findings’) Confusion of ‘disorders’ with ‘morphological abnormalities’

  18. SNOMED CT 128477000 Abscess (disorder) 44132006 Abscess (morphologic abnormality)

  19. Epistemology and Combinatorial Explosion • Epistaxis/nosebleed • Epistaxis (disorder) • Nosebleed/epistaxis symptom (finding) • On examination - epistaxis (disorder) • Has nosebleeds - epistaxis (disorder) • Evidence of recent epistaxis (finding) from Bill Hogan

  20. Epistemology and Combinatorial Explosion • Rash • Cutaneous eruption (morphologic abnormality), with synonym Rash • Eruption of skin (disorder), with synonym Rash • Complaining of a rash (finding) • On examination - a rash (finding) • Dry skin • Dry skin (finding) • Complaining of dry skin (finding) • On examination - dry skin (finding) • Dry skin dermatitis (disorder) from Bill Hogan

  21. An Alternative: Basic Formal Ontology • 360 BC: Aristotle’s Metaphysics • 1879: Invention of modern logic (Boole, • Frege) • 1920: The problem of the Unity of Science (Logical Positivism) • 1940 Birth of computing (Turing) http://ontology.buffalo.edu/smith

  22. Ontology Timeline • 1970: AI, Robotics (J. McCarthy, P. Hayes) • 1980: KIF: Knowledge Interchange Format • 1990: Description Logics • 2000: Semantic Web (OWL), Protégé • 2007: National Center for Biomedical Ontology (NCBO) Bioportal http://ontology.buffalo.edu/smith

  23. Uses of ‘ontology’ in PubMed abstracts

  24. Biomedical Ontology in PubMed

  25. By far the most successful: GO (Gene Ontology)

  26. Ontology Timeline • 1990: Human Genome Project • 1999: The Gene Ontology (GO) – Model Organism Research • 2005: The Open Biomedical Ontologies • (OBO) Foundry • 2010: Ontology for General Medical • Science http://ontology.buffalo.edu/smith

  27. The GO is a controlled vocabulary for use in annotating data • multi-species, multi-disciplinary, open source • contributing to the cumulativity of scientific results obtained by distinct research communities • compare use of kilograms, meters, seconds … in formulating experimental results

  28. NIH Mandates for Data Sharing Organizations such as the NIH now require use of common standards in a way that will ensure that the results obtained through funded research are more easily accessible to external groups. ODR will be created in such a way that its use will address the new NIH mandates. It will designed also to allow information presented in its terms to be usable in satisfying other regulatory purposes—such as submissions to FDA. http://ontology.buffalo.edu/smith

  29. GO provides answers to three types of questions: for each gene product (protein ...) • in what parts of the cell has it been identified? Cell Constituent Ontology • exercising what types of molecular functions? Molecular Function Ontology • with what types of biological processes? Biological Process Ontology

  30. = part_of • = subtype_of • Gene Product • Associations

  31. $100 mill. invested in literature curation using GO over 11 million annotations relating gene products described in the UniProt, Ensembl and other databases to terms in the GO ontologies provide the basis for capturingbiologicaltheories in computable form in contrast to terminologies and thesauri – which focus on socially diverse uses of language – the GO methodfocuses on commonlysharedresults of basic biological science

  32. A new kind of biological researchbased on analysis and comparison of the massive quantities of annotations linking ontology terms to raw data, including genomic data, clinical data, public health dataWhat 10 years ago took multiple groups of researchers months of data comparison effort, can now be performed in milliseconds

  33. The GO covers only generic (‘normal’) biological entities of three sorts: • cellular components • molecular functions • biological processes It does not provide representations of diseases, symptoms, genetic abnormalities … How to extend the GO methodology to other domains of biology and medicine?

  34. The Open Biomedical Ontologies (OBO) Foundry

  35. OBO Foundry ontologies all follow the same principles to ensure interoperability • GO Gene Ontology • ChEBI Chemical Ontology • PRO Protein Ontology • CL Cell Ontology • ... • OGMS Ontology for General Medical Science

  36. Basic Formal Ontology: GO at a high level http://ontology.buffalo.edu/smith

  37. Basic Formal Ontology (BFO) A simple top-level ontology to support information integration in scientific research No abstracta Nothing propositional Clear hierarchy No overlap with domain ontologies No confusion of ontology with epistemology No confusion of terms with what terms represent in reality

  38. Basic Formal Ontology Continuant Occurrent (Process, Event) Independent Continuant Dependent Continuant http://ifomis.uni-saarland.de/bfo/

  39. BFO and the 3 Gene Ontologies (GO) Continuant Occurrent Biological Process Independent Continuant Dependent Continuant Cell Component Molecular Function Kumar A., Smith B, Borgelt C. Dependence relationships between Gene Ontology terms based on TIGR gene product annotations. CompuTerm 2004, 31-38. Bada M, Hunter L. Enrichment of OBO Ontologies. J Biomed Inform. 2006 Jul 26

  40. Users of BFO NCI BiomedGT SNOMED CT Ontology for General Medical Science (OGMS) ACGT Clinical Genomics Trials on Cancer – Master Ontology / Formbuilder (Case Report Forms for Cancer Clinical Trials)

  41. Users of BFO MediCognos / Microsoft Healthvault Cleveland Clinic Semantic Database in Cardiothoracic Surgery Major Histocompatibility Complex (MHC) Ontology (NIAID) Neuroscience Information Framework Standard (NIFSTD) and Constituent Ontologies

  42. Users of BFO Interdisciplinary Prostate Ontology (IPO) Nanoparticle Ontology (NPO): Ontology for Cancer Nanotechnology Research Neural Electromagnetic Ontologies (NEMO) ChemAxiom – Ontology for Chemistry Ontology for Risks Against Patient Safety (RAPS/REMINE) (EU FP7) IDO Infectious Disease Ontology (NIAID)

  43. Infectious Disease Ontology Consortium • MITRE, Mount Sinai, UTSouthwestern – Influenza • IMBB/VectorBase – Vector borne diseases (A. gambiae, A. aegypti, I. scapularis, C. pipiens, P. humanus) • Colorado State University – Dengue Fever • Duke University – Tuberculosis, Staph. aureus • Case Western Reserve – Infective Endocarditis • University of Michigan – Brucellosis

  44. The OBO Foundry • GO Gene Ontology • CL Cell Ontology • SO Sequence Ontology • ChEBI Chemical Ontology • PATO Phenotype (Quality) Ontology • FMA Foundational Model of Anatomy • ChEBI Chemical Entities of Biological Interest • CARO Common Anatomy Reference Ontology • PRO Protein Ontology • Infectious Disease Ontology • Plant Ontology • Environment Ontology • Ontology for Biomedical Investigations • RNA Ontology  

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