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

6. 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

7. 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?

8. 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?