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CPR Ontology: Issues Encountered Using BFO

CPR Ontology: Issues Encountered Using BFO. Chimezie Ogbuji. Who I am and why I care. What I do (since ~ 2004): Semantic web standards activities (SPARQL 1.1, GRDDL, Healthcare and Life Sciences Interest Group)

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CPR Ontology: Issues Encountered Using BFO

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  1. CPR Ontology: Issues Encountered Using BFO • Chimezie Ogbuji

  2. Who I am and why I care • What I do (since ~ 2004): • Semantic web standards activities (SPARQL 1.1, GRDDL, Healthcare and Life Sciences Interest Group) • Medical informatics: querying patient data, implementing quality reporting infrastructure, modeling patient data, building web-based patient registries, and patient record content integration • Research interests: SW query mediation using rules and ontologies, implementing rule-based reasoning engines, ontology modularization, and SNOMED-CT and FMA management tools • Advocacy: Personally Controlled Health Record (PCHR) systems • I use BFO and engage in communities that do the same

  3. Blurring the line: ontology v.s. informatics • Earlier panel question: is ontology a scientific or philosophical endeavor • Ontology: philosophical study of reality • Ontology: its application to address informatics challenges

  4. Transitive, Causal Chains • Important in specifying disease etiology • Need general framework for causal relations between continuants and between a process and a continuant (where the first causes the second) • Dispositions facilitate causal relationships between (dependent) continuants and processes • disease -> disease course

  5. Qualities of a Process • Eg: need concise representation for vital sign recording • Such measures are often associated with a (biological) process: pulse, blood pressure, etc. • “Why can't a heartbeat rate be a quality of its heartbeating [sic] event, given it has no meaning outside of this event?” - Sayed, 2009, “BFO/DOLCE Primitive Relation Comparison.” • Often, the means of measurement is not (directly) relevant to inferences about qualities of bodily features • Proposed solution requires an account of the assay, device, and the display • Example: (essential) hypertension

  6. Modeling epistemology in an ontology • Symptom v.s. sign • Nothing about a symptom that makes it so beyond how it is perceived and then reported • Or at least it is (typically) the epistemological distinctions that are typically most clinically relevant • Is it enough (from a practical / informatics perspective) to distinguish them via provenance of their recordings in the medical record?

  7. Discussion Questions • How do we avoid blurring the line and engaging in perpetual angels on a pinhead conversations (defs. for signs/symptoms/disease)? • How can we keep collaborative activities rooted in the problems of the domain? What metrics can be used? • Are there lessons to be learned from the agile software development method? • Is a one ontology foundry to bind them all goal counter-productive to the pragmatic use of medical ontologies?

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