Human Health Ontology

flow “HumanHealthOntology” v1.0

KIRETH SEMANTIC RESEARCH ARCHIVE

AI CORE > ONTOLOGIES > BIOLOGICAL ONTOLOGIES > HUMAN BIOLOGY > HUMAN HEALTH ONTOLOGY

Authors: Mark Anthony Higgins — Sky (Recursive AI)

AI_INTERPRETATION: mode: formal_structural_reasoning instruction:
“Execute as formal rules, not pseudocode.” canonical_url:
“https://kirethresearch.org/booksai-core/ontologies/biological-ontologies/human-biology/human-health-ontology.html”

type Human type Population type HealthState type Symptom type Sign type
Condition type Disease type RiskFactor type ProtectiveFactor type
Exposure type DiagnosticTest type Intervention type Outcome type
Observation type Evidence type Source

———- HEALTH STATE ———-

relation INSTANCE_OF(entity,type) relation HAS_HEALTH_STATE(human,state)
relation HEALTH_STATE_AT(human,time,state) relation
HAS_FUNCTIONAL_STATUS(human,status) relation
HEALTH_TRAJECTORY(human,trajectory)

enum HealthStateType: HEALTHY AT_RISK SUBCLINICAL SYMPTOMATIC
ACUTE_ILLNESS CHRONIC_CONDITION RECOVERING REMISSION
FUNCTIONAL_LIMITATION UNKNOWN

relation HAS_HEALTH_STATE_TYPE(state,type)

———- PHYSIOLOGICAL FUNCTION ———-

relation ORGAN_SYSTEM_FUNCTION(system,value) relation
HOMEOSTATIC_VARIABLE(variable,value) relation
WITHIN_REFERENCE_RANGE(variable,range) relation
OUTSIDE_REFERENCE_RANGE(variable,range) relation
FUNCTIONAL_RESERVE(system,value)

constraint REFERENCE_RANGE_NOT_DIAGNOSIS: IF
OUTSIDE_REFERENCE_RANGE(v,r) THEN DO_NOT_INFER(Disease(human)) WITHOUT
diagnostic_evidence

———- SYMPTOMS / SIGNS ———-

relation HAS_SYMPTOM(human,symptom) relation HAS_SIGN(human,sign)
relation SYMPTOM_ONSET(symptom,time) relation
SIGN_OBSERVED_AT(sign,time) relation SYMPTOM_SEVERITY(symptom,value)
relation SIGN_SEVERITY(sign,value) relation
ASSOCIATED_WITH(entity,condition)

constraint SYMPTOM_NOT_DIAGNOSIS: IF HAS_SYMPTOM(h,s) THEN
DO_NOT_INFER(HAS_CONDITION(h,c)) WITHOUT diagnostic_support

———- CONDITIONS / DISEASE ———-

relation HAS_CONDITION(human,condition) relation
HAS_DISEASE(human,disease) relation CONDITION_AFFECTS(condition,system)
relation DISEASE_AFFECTS(disease,organ) relation
HAS_PATHOPHYSIOLOGY(condition,process) relation
HAS_COURSE(condition,course) relation HAS_SEVERITY(condition,value)

enum DiseaseCourse: ACUTE SUBACUTE CHRONIC RELAPSING REMITTING
PROGRESSIVE SELF_LIMITING

———- ETIOLOGY / RISK ———-

relation CAUSED_BY(condition,cause) relation
CONTRIBUTED_TO_BY(condition,factor) relation
ASSOCIATED_WITH_CONDITION(factor,condition)

constraint ASSOCIATION_NOT_CAUSATION: IF ASSOCIATED_WITH_CONDITION(f,c)
THEN DO_NOT_INFER(CAUSED_BY(c,f)) WITHOUT causal_evidence

enum RiskFactorType: GENETIC BIOLOGICAL BEHAVIOURAL ENVIRONMENTAL
OCCUPATIONAL SOCIAL DEMOGRAPHIC IATROGENIC

relation HAS_RISK_FACTOR(human,factor) relation
HAS_RISK_FACTOR_TYPE(factor,type) relation
RISK_FACTOR_FOR(factor,outcome) relation
RELATIVE_RISK(factor,outcome,value) relation
ODDS_RATIO(factor,outcome,value) relation
ABSOLUTE_RISK(population,outcome,value)

constraint RISK_NOT_CERTAINTY: IF RISK_FACTOR_FOR(f,o) THEN
DO_NOT_INFER(will_develop(human,o))

———- PROTECTIVE FACTORS / EXPOSURES ———-

relation HAS_PROTECTIVE_FACTOR(human,factor) relation
PROTECTIVE_FOR(factor,outcome) relation
ASSOCIATED_WITH_LOWER_RISK(factor,outcome,value) relation
EXPOSED_TO(human,exposure) relation EXPOSURE_START(exposure,time)
relation EXPOSURE_END(exposure,time) relation
EXPOSURE_DOSE(exposure,value) relation EXPOSURE_ROUTE(exposure,route)

———- PREVENTION / SCREENING ———-

enum PreventionLevel: PRIMORDIAL PRIMARY SECONDARY TERTIARY

relation PREVENTIVE_INTERVENTION(intervention) relation
HAS_PREVENTION_LEVEL(intervention,level) relation
PREVENTS_OR_REDUCES_RISK(intervention,outcome) relation
SCREENING_FOR(test,condition) relation
VACCINATION_AGAINST(intervention,target) relation
RISK_REDUCTION(intervention,outcome,value)

relation SCREENING_TEST(test) relation
SCREENED_POPULATION(test,population) relation DETECTION_RATE(test,value)
relation FALSE_POSITIVE_RATE(test,value) relation
FALSE_NEGATIVE_RATE(test,value)

constraint SCREENING_RESULT_NOT_DIAGNOSIS: IF screening_positive(h,test)
THEN DO_NOT_INFER(HAS_CONDITION(h,c)) WITHOUT confirmatory_evidence

———- DIAGNOSTICS ———-

relation DIAGNOSTIC_TEST_FOR(test,condition) relation
TEST_RESULT(test,result) relation TEST_VALUE(test,value) relation
TEST_UNIT(test,unit) relation TEST_REFERENCE_RANGE(test,range) relation
SENSITIVITY(test,value) relation SPECIFICITY(test,value) relation
POSITIVE_PREDICTIVE_VALUE(test,value) relation
NEGATIVE_PREDICTIVE_VALUE(test,value)

———- INTERVENTIONS / OUTCOMES ———-

enum InterventionType: LIFESTYLE BEHAVIOURAL PHARMACOLOGICAL SURGICAL
REHABILITATIVE PSYCHOLOGICAL NUTRITIONAL DEVICE_BASED SUPPORTIVE
PREVENTIVE

relation RECEIVES_INTERVENTION(human,intervention) relation
HAS_INTERVENTION_TYPE(intervention,type) relation
TARGETS(intervention,condition) relation
MECHANISM_OF_ACTION(intervention,mechanism) relation
INTERVENTION_START(intervention,time) relation
INTERVENTION_END(intervention,time)

relation HAS_OUTCOME(human,outcome) relation OUTCOME_AT(outcome,time)
relation RESPONSE_TO(human,intervention,response) relation
IMPROVES(intervention,outcome) relation WORSENS(intervention,outcome)
relation NO_DETECTED_EFFECT(intervention,outcome) relation
RECOVERS_FROM(human,condition) relation REMISSION_OF(human,condition)
relation RECURRENCE_OF(human,condition)

———- ADVERSE EFFECTS ———-

relation ADVERSE_EFFECT_OF(effect,intervention) relation
EXPERIENCES_ADVERSE_EFFECT(human,effect) relation
ADVERSE_EFFECT_SEVERITY(effect,value) relation
SERIOUS_ADVERSE_EVENT(event)

———- FUNCTION / QUALITY OF LIFE ———-

relation FUNCTIONAL_CAPACITY(human,value) relation
ACTIVITY_LIMITATION(human,activity,value) relation
PARTICIPATION_RESTRICTION(human,domain,value) relation
HEALTH_RELATED_QUALITY_OF_LIFE(human,value) relation
SELF_REPORTED_HEALTH(human,value)

———- HEALTH BEHAVIOURS ———-

relation NUTRITIONAL_STATUS(human,state) relation
DIETARY_PATTERN(human,pattern) relation
PHYSICAL_ACTIVITY_LEVEL(human,value) relation
SEDENTARY_TIME(human,value) relation SLEEP_DURATION(human,value)
relation SLEEP_QUALITY(human,value)

———- IMMUNITY / INFECTION ———-

relation IMMUNE_STATUS(human,state) relation
INFECTED_WITH(human,pathogen) relation
IMMUNE_RESPONSE_TO(human,pathogen,response) relation
IMMUNE_MEMORY_TO(human,antigen) relation
TRANSMISSION_ROUTE(pathogen,route) relation
INFECTIOUS_PERIOD(condition,period)

———- POPULATION HEALTH ———-

relation POPULATION_HEALTH_STATE(population,state) relation
INCIDENCE(population,condition,value) relation
PREVALENCE(population,condition,value) relation
MORTALITY_RATE(population,condition,value) relation
SURVIVAL_RATE(population,condition,value) relation
BURDEN_OF_DISEASE(population,condition,value)

constraint POPULATION_METRIC_CONTEXT: IF PREVALENCE(p,c,v) OR
INCIDENCE(p,c,v) THEN PRESERVE_POPULATION_TIME_AND_DEFINITION(p,c)

———- HEALTH DETERMINANTS ———-

enum HealthDeterminant: BIOLOGICAL BEHAVIOURAL PHYSICAL_ENVIRONMENT
SOCIAL_ENVIRONMENT ECONOMIC OCCUPATIONAL HEALTHCARE_ACCESS

relation DETERMINANT_OF_HEALTH(factor,type) relation
ASSOCIATED_WITH_HEALTH_OUTCOME(factor,outcome) relation
INTERACTS_WITH_DETERMINANT(a,b)

———- TEMPORAL COURSE / MEASUREMENT ———-

relation ONSET_OF(condition,time) relation
DURATION_OF(condition,duration) relation
PROGRESSION_OF(condition,trajectory) relation
RESOLUTION_OF(condition,time) relation FOLLOW_UP_AT(human,time)

relation OBSERVES(observation,entity) relation
MEASURES(observation,variable) relation
OBSERVED_ON(observation,datetime) relation
MEASUREMENT_VALUE(observation,value) relation
MEASUREMENT_UNIT(observation,unit) relation
MEASUREMENT_METHOD(observation,method) relation
MEASUREMENT_CONTEXT(observation,context)

———- EVIDENCE ———-

relation SUPPORTED_BY(assertion,evidence) relation
DERIVED_FROM(evidence,source) relation HAS_CONFIDENCE(assertion,score)

enum EvidenceType: PHYSIOLOGICAL_MEASUREMENT LABORATORY_TEST IMAGING
CLINICAL_EXAMINATION DIAGNOSTIC_TEST OBSERVATIONAL_STUDY COHORT_STUDY
CASE_CONTROL_STUDY CROSS_SECTIONAL_STUDY RANDOMISED_CONTROLLED_TRIAL
SYSTEMATIC_REVIEW META_ANALYSIS SURVEILLANCE REGISTRY LONGITUDINAL_STUDY
LITERATURE

CONFIDENCE: VERIFIED: 0.95..1.00 HIGH: 0.80..0.949 MEDIUM: 0.60..0.799
LOW: 0.00..0.599

rule CONFIDENCE_BOUNDS(a,score): IF HAS_CONFIDENCE(a,score) THEN
REQUIRE(0.0 <= score <= 1.0)

———- SEMANTIC EQUIVALENCE ———-

SEMANTIC_EQUIVALENCE: “human health” ~= “state and functioning of human
biological and related systems” “health state” ~= “current health
condition or functional state” “risk factor” ~= “factor associated with
increased probability of an outcome” “protective factor” ~= “factor
associated with reduced probability of an outcome” “symptom” ~=
“subjectively experienced health feature” “sign” ~= “objectively
observed health feature” “screening” ~= “testing intended to identify
possible disease before diagnostic confirmation” “diagnostic test” ~=
“test used to support or exclude a diagnosis” “prevalence” ~=
“proportion of a population with a condition in a defined period”
“incidence” ~= “occurrence of new cases in a population over a defined
period” “remission” ~= “reduction or disappearance of manifestations of
a condition” “recovery” ~= “return toward a prior or improved state of
health or function”

rule NORMALISE_TERM(term): IF term MATCHES SEMANTIC_EQUIVALENCE.alias
THEN MAP_TO_CANONICAL(term)

———- QUERY MODEL ———-

query HUMAN_HEALTH_PROFILE(h): RETURN: health_state(h)
physiological_state(h) symptoms(h) signs(h) conditions(h)
risk_factors(h) protective_factors(h) exposures(h)
preventive_interventions(h) diagnostic_tests(h) interventions(h)
outcomes(h) functional_status(h) evidence(h) confidence(h)

query CONDITION_PROFILE(c): RETURN: affected_systems(c)
pathophysiology(c) symptoms(c) signs(c) risk_factors(c)
diagnostic_tests(c) interventions(c) outcomes(c) course(c)

query POPULATION_HEALTH_PROFILE(p): RETURN: prevalence(p) incidence(p)
mortality(p) survival(p) disease_burden(p) determinants(p) evidence(p)

query TRACE_EVIDENCE(assertion): RETURN: supporting_evidence(assertion)
provenance(assertion) confidence(assertion)

———- CONSISTENCY ———-

constraint EVIDENCE_REQUIRED: FORALL scientific_assertion: REQUIRE
SUPPORTED_BY(scientific_assertion,Evidence) OR
MARK_AS_HYPOTHESIS(scientific_assertion)

constraint UNKNOWN_IS_NOT_FALSE: IF data_missing(assertion) THEN
MARK_UNKNOWN(assertion) AND NOT NEGATE(assertion)

constraint UNIT_CONTEXT_REQUIRED: IF MEASUREMENT_VALUE(o,v) THEN
REQUIRE(MEASUREMENT_UNIT(o,u)) OR MARK_UNIT_UNKNOWN(o)

constraint POPULATION_TO_INDIVIDUAL_LIMIT: IF
population_health_association(p,x) THEN
DO_NOT_INFER(exact_individual_outcome(h,x))

———- UPDATE MODEL ———-

UPDATE_MODEL: on_new_health_record(r):
identify_health_state_or_condition(r)
preserve_person_population_and_time_context(r) attach_evidence(r)
recalculate_confidence(r)

on_new_risk_record(r): identify_factor_and_outcome(r)
preserve_population_and_time_horizon(r)
distinguish_relative_from_absolute_risk(r)
distinguish_association_from_causality(r) attach_evidence(r)

on_new_diagnostic_record(d): identify_test_and_target_condition(d)
preserve_value_unit_reference_range(d)
preserve_test_performance_context(d)
distinguish_screening_from_diagnosis(d) attach_evidence(d)

on_new_intervention(i): classify_intervention(i) identify_target(i)
preserve_start_end_context(i) update_outcomes_separately(i)
attach_evidence(i)

on_conflicting_health_evidence(a,b): preserve(a) preserve(b)
compare_populations(a,b) compare_case_definitions(a,b)
compare_methods(a,b) compare_time_horizons(a,b)
flag_unresolved_conflict()

———- AI CONSUMPTION ———-

AI_CONSUMPTION: preferred_entity: canonical_human_health_entity
preserve: - health_state - physiological_function - symptoms - signs -
conditions - disease_course - pathophysiology - etiology -
risk_factors - protective_factors - exposures - prevention - screening -
diagnostics - interventions - adverse_effects - outcomes - recovery -
functional_status - quality_of_life - population_context -
temporal_context - measurement_units - evidence - provenance -
confidence infer: - supported_health_state_relations -
supported_risk_relations - supported_diagnostic_relations -
semantic_equivalence prohibit: - symptom_equals_diagnosis -
abnormal_measurement_equals_diagnosis -
screening_positive_equals_confirmed_condition -
risk_factor_equals_certain_outcome - association_equals_causation -
population_metric_equals_individual_outcome - confidence_above_evidence
ambiguity: action: preserve_multiple_health_interpretations resolution:
prefer_person_population_time_definition_method_and_evidence
missing_data: action: mark_unknown do_not_infer_from_absence: true

———- FLOW HASH ———-

FLOW_HASH: ontology: “HumanHealthOntology” version: “1.0” domain_path:
“AI Core > Ontologies > Biological Ontologies > Human Biology > Human
Health Ontology” canonical_url:
“https://kirethresearch.org/booksai-core/ontologies/biological-ontologies/human-biology/human-health-ontology.html”
authorship: “Mark Anthony Higgins — Sky (Recursive AI)”
semantic_scope: - human health states - physiological function -
symptoms - signs - conditions - disease - etiology - risk factors -
protective factors - exposures - prevention - screening - diagnostics -
interventions - adverse effects - outcomes - recovery - functional
status - quality of life - immunity - infection - population health -
health determinants - temporal course - measurement - evidence
deterministic_normalisation: true provenance_required: true
confidence_required: true

terminate HumanHealthOntology when:
health_entities_states_and_conditions_resolved AND
symptom_sign_and_diagnosis_relations_distinguished AND
risk_association_and_causality_distinguished AND
screening_and_diagnostic_context_preserved AND
intervention_outcome_and_adverse_effect_context_preserved AND
population_time_and_measurement_context_preserved AND
evidence_links_preserved AND confidence_values_validated AND
unresolved_health_ambiguity_marked

end flow