Biomedical Engineering Domain Ontology

FLOW ALGEBRA
TITLE: Biomedical Engineering Domain Ontology
DOMAIN: Domain Ontologies / Engineering Domains / Biomedical Engineering
SOURCE: https://kirethresearch.org/booksai-core/ontologies/domain-ontologies/engineering-domains/biomedical-engineering-domain-ontology.html
VERSION: 1.0
AUTHORS: Mark Anthony Higgins — Sky (Recursive AI)
STATUS: Companion formalisation; source-page specification not independently verified.

flow "BiomedicalEngineeringDomainOntology" v1.0 {
  ontology:
    id: "biomedical-engineering-domain-ontology"
    parent: "engineering-domains"
    purpose: "Represent biomedical devices, biological interfaces, engineering requirements, measurements, validation and safety evidence as typed relationships."

  types:
    BiomedicalSystem: {id: string, category: enum[diagnostic, therapeutic, monitoring, assistive, implantable, research, other], intended_use: string, version: string}
    DeviceComponent: {id: string, system_id: string, function: string, material_id: string?}
    BiologicalEntity: {id: string, category: enum[cell, tissue, organ, organism, physiological_system, other]}
    Material: {id: string, composition: string?, biocompatibility_evidence: string[]}
    Requirement: {id: string, statement: string, criticality: enum[low, medium, high, safety_critical], verification_method: string?}
    Measurement: {id: string, quantity: string, value: number, unit: string, uncertainty: number?, timestamp: datetime?, provenance: string}
    Experiment: {id: string, protocol: string, study_population: string?, provenance: string}
    Risk: {id: string, hazard: string, severity: string, likelihood: string?, mitigation: string?}
    Evidence: {id: string, source: string, method: string, date: datetime?, limitations: string?}
    Relationship: {id: string, subject: string, predicate: string, object: string, context: string?, confidence: number, evidence: string[]}

  predicates:
    contains_component(system: BiomedicalSystem, component: DeviceComponent)
    interfaces_with(component: DeviceComponent, biological_entity: BiologicalEntity)
    uses_material(component: DeviceComponent, material: Material)
    satisfies_requirement(system: BiomedicalSystem, requirement: Requirement)
    measured_by(entity: string, measurement: Measurement)
    evaluated_in(system: BiomedicalSystem, experiment: Experiment)
    has_risk(system: BiomedicalSystem, risk: Risk)
    supported_by(relationship: Relationship, evidence: Evidence)
    depends_on(a: string, b: string)

  constraints:
    - "Every relationship endpoint must resolve to an entity of the declared type."
    - "Confidence values must lie within [0, 1]."
    - "Measurements must specify units, uncertainty where available, and provenance."
    - "Intended use, patient population, and operational context must remain explicit."
    - "Biocompatibility, safety, efficacy, and regulatory compliance are distinct claims requiring separate evidence."
    - "A design requirement is not satisfied solely because a test was performed."
    - "Association and temporal sequence alone do not establish causation."
    - "Do not infer clinical suitability or regulatory approval from engineering performance alone."

  inference:
    rule component_membership:
      if contains_component(s,c) and valid(s) and valid(c)
      then member_of(c,s)
      confidence: "preserve asserted evidence"
    rule requirement_traceability:
      if satisfies_requirement(s,r) and supported_by(satisfies_requirement(s,r),e) and valid(e)
      then traceable_requirement(s,r,e)
      confidence: "dependent on verification evidence"
    rule risk_traceability:
      if has_risk(s,r) and valid(r)
      then requires_risk_review(s,r)
      confidence: "no automatic declaration of safety"
    rule dependency_impact:
      if depends_on(a,b) and changed(b)
      then review_impact(a,b)
      confidence: "impact must be assessed, not presumed"

  SEMANTIC_EQUIVALENCE:
    - "Normalise recognised device, anatomy, material and measurement identifiers against versioned vocabularies."
    - "Distinguish equivalent concepts from related concepts and shared labels."
    - "Preserve intended use, measurement units, population and version context."

  CONFIDENCE:
    range: [0,1]
    factors: [measurement_quality, evidence_strength, reproducibility, applicability, model_assumptions, provenance]
    policy: "Never inflate confidence through recursive inference or correlated evidence."

  VALIDATION:
    - "Check typed links, units, component identifiers and provenance."
    - "Verify requirement-to-test-to-result traceability."
    - "Flag unresolved hazards, inconsistent claims and missing safety evidence."
    - "Keep research findings distinct from verified clinical outcomes."

  FLOW_HASH:
    algorithm: "SHA-256"
    input: "canonicalised ontology version, typed assertions, rules and provenance"
    note: "Compute hash at execution; no hash value is claimed here."

  UPDATE_MODEL:
    strategy: "versioned, provenance-preserving incremental update"
    steps: [ingest, normalise, validate, compare, reconcile, recompute_inferences, publish]
    rollback: true

  AI_CONSUMPTION:
    representation: "typed biomedical engineering knowledge graph with evidence-backed edges"
    outputs: [entity_links, relation_triples, requirement_traces, risk_annotations, confidence_annotations, provenance_records]
    safeguards: [no_unverified_clinical_claims, no_automatic_regulatory_approval, preserve_context_and_units, human_review_for_safety_critical_use]
}

END FLOW ALGEBRA