primaryNeoplasmdp back to ToC or Data Property ToC
IRI: https://w3id.org/brainteaser/ontology/schema/primaryNeoplasm
- has domain
- ALS Brainteaser Comorbidities c
- has range
- boolean
This webpage describes the design and development of the BrainTeaser Ontology (BTO) whose purpose is to jointly model both Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) Clinical Data.
The BTO serves multiple purposes:
The BTO is innovative since it relies on very few seed concepts - Patient, Clinical Trial, Disease, Event - which allow us to jointly model ALS and MS and to grasp the time dimension entailed by the progression of such diseases.
Indeed, the core idea is that a Patient participates in a Clinical Trial, suffers from some Diseases, and undergoes Events. These Events are different in nature and cover a wide range of cases, e.g. Onset, Pregnancy, Symptom, Trauma, Diagnostic Procedure (like evoked potentials or ALS-FRS questionnaires) Therapeutic Procedure (like Mechanical Ventilation for ALS or Disease-Modifying Therapy for MS), Relapse, and more. Overall, this event-based approach allows us to model ALS and MS in an unified way, sharing concepts among these two diseases, and to track what happens during their progression. Details of the design and functioning of the BTO are provided in the next sections of this deliverable.
The BTO plays an important role in the overall BRAINTEASER architecture, shown in Figure 1. Indeed, it informs the implementation of the BRAINTEASER Semantic Data Cloud since the data contained here will be represented according to the BTO, i.e., they will be an instance of the BTO.
In Figure 1 it is possible to see that all the data will be anonymized prior to being represented in the BTO. This holds true for both the retrospective data, i.e., the data already held by clinical partners on the right of the figure, and the prospective data, i.e., the new data that will be collected during the project lifetime on the left.
The data held in the BRAINTEASER Semantic Data Cloud, exported in a suitable format, will then be used to train the AI models needed to predict the progression of both ALS and MS.
Finally, a subset of the data in the BRAINTEASER Semantic Data Cloud will be exported to the European Open Science Cloud (EOSC) and they will be also shared and exploited for the Open Evaluation Challenges.
Figure 2 shows an overall picture of the whole data flow associated with the BTO and the BRAINTEASER Semantic Data Cloud, also in relation to Open Evaluation Challenges and the EOSC.
In summary, the BTO ontology will model the following data sources:
The BTO will also allow to link these data with other resources available in the Linked Open Data Cloud.
The term ontology has its origin in philosophy, where it refers to the study of existence. Similarly, in Computer Science an ontology is an explicit specification of a conceptualization [8]. Since their conception, ontologies have been largely employed to share a common understanding of the structure of information among people and software agents [9, 10]. On top of that, ontologies allow computers to have access to structured collections of information and rules that can be used to conduct automated reasoning. For these reasons, ontologies are considered the third basic component of the Semantic Web [9, 11]. Depending on the generality of the instances included within ontologies, we can distinguish between foundation and domain ontologies. The former model com- mon concepts that can be used in a wide variety of domains. A notable example is the Friend-Of-A-Friend (FOAF) ontology, which describes people’s personal information as well as their social relationships [11]. In general, foundation ontologies are thought to be imported and reused in domain-specific applications, where the original components are refined and extended to better represent the modeled reality [10]. The latter model a specific domain, thus representing the particular meaning of terms as they apply to that specialization. Indeed, the same word can assume different meanings in different fields. To illustrate this, let’s take the word “polyp” as an example. In the medical domain, a polyp is an abnormal growth of tissue projecting from a mucous membrane. On the other hand, in zoology, a polyp represents one of the two forms found in the Phylum Cnidaria. Given the objectives of this work, below we focus on describing domain ontologies.
External Referencing Whenever we model clinical information, it is preferable to use external ontologies to provide correspondence between largely used medical terminology. Due to its widely diffused adoption and exhaustiveness, our primary taxonomy of choice is NCIT [30], but we also employed other well-known resources when no information is available in NCIT, e.g. SNOMED-CT or ATC. In general, this is common practice when developing ontologies [44]. In fact, by reusing entities and properties already defined in other ontologies, we are not only able to enforce collaboration and data consistency among databases but to guarantee the authori- tativeness of the semantic meaning of these resources. External referencing has been managed with annotation properties and using the URI of the term in the original taxonomy. In general, we used external URIs when we defined named individuals that refer to abstract concepts. On the contrary, when we inserted a new class in BTO we used the Brainteaser namespace and connected references using annotation properties.
Namespaces BTO URIs are divided into two namespaces: the schema namespace https://w3id.org/brainteaser/ontology/schema/ and the resource namespace https://w3id.org/brainteaser/ontology/resource/. All URIs corresponding to classes, data properties, and object properties belong to the former namespace. At the same time, in the latter, we include all URIs referring to real-world instances of the entities described in BTO at an ontological level. Notice that, in this sense, the resource namespace is empty until the practitioner starts populating it with real-world data. The only instances included in the schema namespace are the named individuals corresponding to Simple Knowledge Organization System (SKOS) con- cepts (as defined in Subsection 3.4). Our choice of including these elements in the schema namespace stems from the fact that, akin to relational modeling controlled dictionaries, these entities do not depend on the data underneath but can be seen as a predefined thesaurus of concepts and are a fundamental part of the environment that we model.
Classes Definition and Annotation Properties All components of the BTO have additional information in the form of annotation properties. We defined a list of essential metadata to add when we introduced a new class. Firstly, all classes must have a label denoting the name and a comment, which provides a brief explana- tion together with its source. If the class had an equivalent in NCIT, we inherited the name and definition. In this case, we also added another annotation property called “rdfs:isDefinedBy with the Internationalized Resource Identifier (IRI) corre- sponding to the NCIT term of reference. Most biomedical vocabularies are mapped in the UMLS with a unique identifier called Concept Unique Identifier (CUI) [40]. For each class that had a UMLS reference, we added the annotation property “dcterms:conformsTo” with the URL of the corresponding concept.
Naming Conventions Regarding object and data properties, we applied a similar approach and defined the needed annotations and naming conventions to provide uniformity throughout BTO. In particular, all components must have a label and a comment. About object properties, we used explanatory labels where we included the property range. In this case, the comment explains the relationship between the two classes involved. Concerning data properties, the label usually includes the name of the domain class so that its meaning is intuitive. We also added a comment with the attribute description and, when available, the definition source. In conclusion, we defined all data properties to achieve consistency when specifying datatypes. For example, all string properties have datatype “rdf:PlainLiteral”. We also used the “note” annotation property in all BTO components for additional remarks or business logic rules.
Class | Reference Taxonomy |
---|---|
Kinship Type | National Cancer Institute Thesaurus (NCIT) |
Occupation | Occupations pillar of the ESCO Classification (ESCO) |
Group (social concept) | SNOMED Clinical Terms (SNOMED-CT) |
Gene | National Cancer Institute Thesaurus (NCIT) |
Disease, Disorder or Findings | National Cancer Institute Thesaurus (NCIT) |
Surgical Procedure Type | SNOMED Clinical Terms (SNOMED-CT) |
Therapeutic Procedure Type | National Cancer Institute Thesaurus (NCIT) |
Anatomic Site | National Cancer Institute Thesaurus (NCIT) |
Pharmacological Substance | Anatomical Therapeutic Chemical (ATC) Classification |
Advesre Drug Reaction | Ontology of Adverse Events (OAE) |
Figure 3: Patient semantic area. “Patient” is a subclass of the class Person from FOAF ontology. We directly connect to patient data about genetic mutations, occupation, family history, and residence place. The latter is useful to link environmental information to each patient, which is modeled by importing the “Global City Indicator Environment Ontology” [38].
Environmental Data
Besides genetic predisposition, lifestyle and environmental factors are shown to
affect the risk of MS. Once we acknowledge such correlation, some of these environmental
and lifestyle factors can be modified with prevention measures, especially
for people at greater risk [45]. On the other hand, identifying environmental factors
linked to ALS has proven to be challenging and it still lacks large cohorts. In both
cases, understanding the interaction between genetic and environmental factors can
be a great resource for integrating precision medicine into ALS or MS care [46].
That is why connecting environmental data to each patient in BTO is a fundamental
requirement. These environmental data include pollution information for a given
location, recorded by a weather station. To store this piece of information, in BTO
the practitioner can record each patient’s birth and residence places using the class
“Place”. To model environmental data, we imported Global City Indicator Environment
Ontology[38], also known as Pollution ontology, an ontology designed to
represent ontologically environmental data registered by weather stations. In par-
ticular, we connected a weather station, modeled with the class “Sensing Device”
from the above-mentioned ontology, to a location, modeled with the class “Place”,
with the object property “:coveredPlace”, to model the fact that a station registers
environmental data for a given location. We report how we integrated air pollutants in
Figure 4. The concentration of each air pollutant of interest is
modeled as a subclass of “Air Pollution Concentration”. Notice that we are interested in
storing information about Particulate Matter (PM) 10, PM 2.5, Ozone (O3), Nitrogen
Dioxide (NO2), Sulphur Dioxide (SO2), and Carbon Monoxide (CO) thus we
created a class in our namespace for pollutants not represented in the imported
ontology, such as CO. Air pollution concentrations are produced by sensing de-
vices, thus we connected the classes “Air Pollution Concentration” and “Sensing
Device” with the property “ssn:isProducedBy”. The concentration value of
each air pollutant is stored using the data property “numerical value”, while
the date of the measure is referred to with the object property ":APConcentrationTIme" that links
“Air Pollution Concentration” class to class “time:Instant”. All classes, object
properties, and data properties mentioned above are imported from the “Global
City Indicator Environment Ontology”[38].
Figure 4: Environmental data integration using the imported “Global City Indicator
Environment Ontology” [38]. We linked the class “Place” with “Sensing
Device”, which produces pollution concentration data. Such information
is stored using one of the subclasses of “Air Pollution Concentration”, which
represents the specific pollutant.
Figure 5: Event semantic area. Each patient is connected to clinical history and lifestyle information through the class Event. In fact, using such nodes physicians can store data about habits, past traumas, any clinical event (class “Condition”), coexisting medical conditions (called “comorbidities”), or pregnancies and MS patients’ relapses.
Disease, Disorder or Finding Taxonomy
The class “Disease, Disorder or Finding” includes diseases, like carcinoma or chickenpox infection, but also injuries, symptoms, and findings. This allows modeling and storing in a KB any sort of clinical event that occurred to a patient, even if it is not directly linked to ALS or MS. We modeled this class following the SKOS data model, using NCIT as reference taxonomy, with a few additions from SNOMED-CT thesaurus. As illustrated in Figure 5, we modeled past traumas with the class “Trauma”. However, some injuries have also been made available in the “Disease, Disorder or Finding” taxonomy. As a general rule, we use the taxonomy whenever the trauma is specific (e.g., “Open Dislocation of the Shoulder”). On the other hand, if the clinician knows only that a patient suffered from generic trauma to the shoulder, we instantiate an individual of type “Trauma” and connect it to its Anatomic Site (e.g., “shoulder”) with the property “:anatomicalLocation”. Notice that, thanks to SKOS characteristics, specific traumas (e.g., “open dislocation of the shoulder”) are in relation “:hasBroaderTransitive” with the general concept “injury”. We follow a similar line of thought to deal with symptoms. In this case, we use the taxonomy to model cases where the practitioner needs to store information about general symptoms (e.g., headache, fever). A particular group of symptoms that required deeper modeling granularity is the one that constitutes the onset of the disease (either ALS or MS). Given the high relevance that such symptoms have on the course of the disease, they have not been modeled using the symptoms taxonomy but as data properties of the class “Onset”.
Figure 6: “Intervention or Procedures” area. We distinguish between surgical procedures, diagnostic procedures, and therapeutic procedures. Both surgical and therapeutic interventions are the same for MS and ALS patients while diagnostic procedures can differ based on the disease.
Figure 7: “Therapeutic Procedures” area. The core of the area is the “Therapeutic Treatment” class, where one can store information about drug administrations and its specific aspects, such as dose, frequency and end treatment. In particular, we modeled the “End Therapy Reason” as a class so that we can link possible causes, such as adverse events and pregnancy.
IRI: https://w3id.org/brainteaser/ontology/schema/Activity
IRI: https://w3id.org/brainteaser/ontology/schema/Administration
IRI: https://w3id.org/brainteaser/ontology/schema/AdverseDrugReaction
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#Air_pollution
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#Air_pollution_concentration
IRI: https://w3id.org/brainteaser/ontology/schema/ALSFRS
IRI: https://w3id.org/brainteaser/ontology/schema/AnatomicalSite
IRI: https://w3id.org/brainteaser/ontology/schema/AtmosphericPressure
IRI: https://w3id.org/brainteaser/ontology/schema/AwajiClinicalEvaluation
IRI: https://w3id.org/brainteaser/ontology/schema/BeatToBeatData
IRI: https://w3id.org/brainteaser/ontology/schema/BeforeOnset
IRI: https://w3id.org/brainteaser/ontology/schema/C6H6_concentration
IRI: https://w3id.org/brainteaser/ontology/schema/BVMTR
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#CH4
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#CH4_concentration
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#CO2
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#CO2_concentration
IRI: https://w3id.org/brainteaser/ontology/schema/CVLTII
IRI: https://w3id.org/brainteaser/ontology/schema/CO_concentration
IRI: https://w3id.org/brainteaser/ontology/schema/ClinicalEvaluation
IRI: https://w3id.org/brainteaser/ontology/schema/ClinicalTrial
IRI: https://w3id.org/brainteaser/ontology/schema/ClinicalTrialParticipation
IRI: https://w3id.org/brainteaser/ontology/schema/ClinicsAndHospitals
IRI: https://w3id.org/brainteaser/ontology/schema/CognitiveAssessment
IRI: http://www.w3.org/2004/02/skos/core#Collection
IRI: https://w3id.org/brainteaser/ontology/schema/Comorbidity
IRI: http://www.wurvoc.org/vocabularies/om-1.8/Compount_unit
IRI: http://www.w3.org/2004/02/skos/core#Concept
IRI: http://www.w3.org/2004/02/skos/core#ConceptScheme
Thesauri, classification schemes, subject heading lists, taxonomies, 'folksonomies', and other types of controlled vocabulary are all examples of concept schemes. Concept schemes are also embedded in glossaries and terminologies.
IRI: https://w3id.org/brainteaser/ontology/schema/Condition
IRI: https://w3id.org/brainteaser/ontology/schema/CSFAnalysis
IRI: http://www.wurvoc.org/vocabularies/om-1.8/cubic_metre
IRI: https://w3id.org/brainteaser/ontology/schema/AvgRelHumidity
IRI: https://w3id.org/brainteaser/ontology/schema/AvgSeaLvlPressure
IRI: https://w3id.org/brainteaser/ontology/schema/MaxTemp
IRI: https://w3id.org/brainteaser/ontology/schema/MeanGlobRadiation
IRI: https://w3id.org/brainteaser/ontology/schema/MeanTemp
IRI: https://w3id.org/brainteaser/ontology/schema/MeanWindSpeed
IRI: https://w3id.org/brainteaser/ontology/schema/MinTemp
IRI: https://w3id.org/brainteaser/ontology/schema/PrecipitationSum
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#decibel
IRI: https://w3id.org/brainteaser/ontology/schema/Diagnosis
IRI: https://w3id.org/brainteaser/ontology/schema/DiagnosticImaging
IRI: https://w3id.org/brainteaser/ontology/schema/DiagnosticProcedure
IRI: https://w3id.org/brainteaser/ontology/schema/DiseaseDisorderOrFinding
IRI: https://w3id.org/brainteaser/ontology/schema/DYMUS
IRI: https://w3id.org/brainteaser/ontology/schema/ECAS
IRI: https://w3id.org/brainteaser/ontology/schema/EndTherapyReason
IRI: https://w3id.org/brainteaser/ontology/schema/EQ5D5L
IRI: https://w3id.org/brainteaser/ontology/schema/Event
IRI: https://w3id.org/brainteaser/ontology/schema/EvokedPotentials
IRI: https://w3id.org/brainteaser/ontology/schema/EDSS
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#GHGs
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#GHGs_concentration
IRI: https://w3id.org/brainteaser/ontology/schema/Gene
IRI: https://w3id.org/brainteaser/ontology/schema/GeneticTesting
IRI: https://w3id.org/brainteaser/ontology/schema/GlobalRadiation
IRI: https://w3id.org/brainteaser/ontology/schema/GoldCoastCriteria
IRI: https://w3id.org/brainteaser/ontology/schema/Group
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#HFCs
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#HFCs_concentration
IRI: https://w3id.org/brainteaser/ontology/schema/HandDexterity
IRI: https://w3id.org/brainteaser/ontology/schema/HeartRateMeasurement
IRI: https://w3id.org/brainteaser/ontology/schema/HematologyTest
IRI: https://w3id.org/brainteaser/ontology/schema/HADS
IRI: https://w3id.org/brainteaser/ontology/schema/HADS_ALS
IRI: https://w3id.org/brainteaser/ontology/schema/Humidity
IRI: https://w3id.org/brainteaser/ontology/schema/Impairment
IRI: http://www.w3.org/2006/time#Instant
IRI: http://www.w3.org/2006/time#Interval
IRI: https://w3id.org/brainteaser/ontology/schema/InterventionOrProcedure
IRI: https://w3id.org/brainteaser/ontology/schema/KingsClinicalStaging
IRI: https://w3id.org/brainteaser/ontology/schema/Kinship
IRI: https://w3id.org/brainteaser/ontology/schema/KinshipType
IRI: https://w3id.org/brainteaser/ontology/schema/LaboratoryTestResult
IRI: https://w3id.org/brainteaser/ontology/schema/Last5YearsEvent
IRI: https://w3id.org/brainteaser/ontology/schema/Lifestyle
IRI: https://w3id.org/brainteaser/ontology/schema/MRI
IRI: http://www.wurvoc.org/vocabularies/om-1.8/Mass
IRI: https://w3id.org/brainteaser/ontology/schema/MenstrualCycle
IRI: http://www.wurvoc.org/vocabularies/om-1.8/microgram
IRI: http://ontology.eil.utoronto.ca/GCI/Foundation/GCI-Foundation.owl#microgram_per_cubic_metre
IRI: https://w3id.org/brainteaser/ontology/schema/MiToSFunctionalStaging
IRI: https://w3id.org/brainteaser/ontology/schema/MFIS
IRI: https://w3id.org/brainteaser/ontology/schema/MoreThan5YearsEvent
IRI: https://w3id.org/brainteaser/ontology/schema/MSQOL54
IRI: https://w3id.org/brainteaser/ontology/schema/MuscleToneALS
IRI: https://w3id.org/brainteaser/ontology/schema/MuscleToneAssessment
IRI: https://w3id.org/brainteaser/ontology/schema/MuscleToneMS
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#NO2
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#N2O
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#N2O_concentration
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#NO2_concentration
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#Noise_pollution
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#Noise_pollution_concentration
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#O3
IRI: https://w3id.org/brainteaser/ontology/schema/Occupation
IRI: https://w3id.org/brainteaser/ontology/schema/OligoclonalBands
IRI: https://w3id.org/brainteaser/ontology/schema/Onset
IRI: http://www.w3.org/2004/02/skos/core#OrderedCollection
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#O3_concentration
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PFCs
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PFCs_concentration
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PM
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PM10
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PM10_Mass
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PM10_Volume
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PM2.5
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PM2.5_Mass
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PM2.5_Volume
IRI: https://w3id.org/brainteaser/ontology/schema/PM1_concentration
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PM10_concentration
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#PM2.5_concentration
IRI: https://w3id.org/brainteaser/ontology/schema/Patient
IRI: http://www.w3.org/2000/10/swap/pim/contact#Person
IRI: https://w3id.org/brainteaser/ontology/schema/PharmacologicSubstance
IRI: https://w3id.org/brainteaser/ontology/schema/PhysicalActivity
IRI: https://w3id.org/brainteaser/ontology/schema/PhysicalActivityData
IRI: https://w3id.org/brainteaser/ontology/schema/PSQI
IRI: https://w3id.org/brainteaser/ontology/schema/Place
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#Pollution
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#Pollution_average
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#Pollution_concentration
IRI: https://w3id.org/brainteaser/ontology/schema/PET
IRI: https://w3id.org/brainteaser/ontology/schema/Precipitation
IRI: https://w3id.org/brainteaser/ontology/schema/Pregnancy
IRI: https://w3id.org/brainteaser/ontology/schema/ProtocolEvent
IRI: https://w3id.org/brainteaser/ontology/schema/PulmonaryFunctionTest
IRI: https://w3id.org/brainteaser/ontology/schema/PFTs
IRI: http://www.wurvoc.org/vocabularies/om-1.8/Quantity
IRI: https://w3id.org/brainteaser/ontology/schema/Questionnaire
IRI: https://w3id.org/brainteaser/ontology/schema/AppQuestionnaire
IRI: https://w3id.org/brainteaser/ontology/schema/Relapse
IRI: https://w3id.org/brainteaser/ontology/schema/Residence
IRI: https://w3id.org/brainteaser/ontology/schema/RespiratoryData
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#SF6
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#SF6_concentration
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#SO2
IRI: https://w3id.org/brainteaser/ontology/schema/SelfALSFRSR
IRI: http://purl.oclc.org/NET/ssnx/ssn#SensingDevice
IRI: http://purl.oclc.org/NET/ssnx/ssn#Sensor
IRI: http://www.wurvoc.org/vocabularies/om-1.8/Singular_unit
IRI: https://w3id.org/brainteaser/ontology/schema/SleepData
IRI: https://w3id.org/brainteaser/ontology/schema/SLEEP_ALS
IRI: https://w3id.org/brainteaser/ontology/schema/Smoking
IRI: https://w3id.org/brainteaser/ontology/schema/SPO2Data
IRI: https://w3id.org/brainteaser/ontology/schema/StressLevel
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#SO2_concentration
IRI: https://w3id.org/brainteaser/ontology/schema/SurgicalProcedure
IRI: https://w3id.org/brainteaser/ontology/schema/SurgicalType
IRI: https://w3id.org/brainteaser/ontology/schema/SDMT
IRI: https://w3id.org/brainteaser/ontology/schema/Symptom
IRI: https://w3id.org/brainteaser/ontology/schema/Temperature
IRI: https://w3id.org/brainteaser/ontology/schema/TherapeuticProcedure
IRI: https://w3id.org/brainteaser/ontology/schema/TherapeuticProcedureType
IRI: https://w3id.org/brainteaser/ontology/schema/TherapeuticTreatment
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#tonne
IRI: https://w3id.org/brainteaser/ontology/schema/Trauma
IRI: http://www.wurvoc.org/vocabularies/om-1.8/Unit_division
IRI: http://www.wurvoc.org/vocabularies/om-1.8/Unit_multiple_or_submultiple
IRI: http://www.wurvoc.org/vocabularies/om-1.8/Unit_of_measure
IRI: https://w3id.org/brainteaser/ontology/schema/UseCaseComorbidities
IRI: https://w3id.org/brainteaser/ontology/schema/VisualAcuity
IRI: https://w3id.org/brainteaser/ontology/schema/VAS
IRI: https://w3id.org/brainteaser/ontology/schema/VAS_ALS
IRI: https://w3id.org/brainteaser/ontology/schema/VOC_concentration
IRI: http://www.wurvoc.org/vocabularies/om-1.8/Volume
IRI: https://w3id.org/brainteaser/ontology/schema/WalkTest
IRI: https://w3id.org/brainteaser/ontology/schema/WalkTestALS
IRI: https://w3id.org/brainteaser/ontology/schema/WalkTestMS
IRI: http://ontology.eil.utoronto.ca/GCI/Environment/Pollution.owl#Water_pollution
IRI: https://w3id.org/brainteaser/ontology/schema/WearableDataMeasurement
IRI: https://w3id.org/brainteaser/ontology/schema/WeatherData
IRI: https://w3id.org/brainteaser/ontology/schema/WEIGHT
IRI: https://w3id.org/brainteaser/ontology/schema/WindSpeed
IRI: https://w3id.org/brainteaser/ontology/schema/administration
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IRI: http://www.w3.org/2004/02/skos/core#inScheme
IRI: http://www.w3.org/2004/02/skos/core#semanticRelation
IRI: http://purl.oclc.org/NET/ssnx/ssn#isProducedBy
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IRI: https://w3id.org/brainteaser/ontology/schema/observedResult
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IRI: https://w3id.org/brainteaser/ontology/schema/residenceStart
IRI: https://w3id.org/brainteaser/ontology/schema/respirationAlpha1DimMean
IRI: https://w3id.org/brainteaser/ontology/schema/respirationAlpha1DimRange
IRI: https://w3id.org/brainteaser/ontology/schema/respirationAlpha1ExpMean
IRI: https://w3id.org/brainteaser/ontology/schema/respirationAlpha1ExpRange
IRI: https://w3id.org/brainteaser/ontology/schema/respirationAlpha2DimMean
IRI: https://w3id.org/brainteaser/ontology/schema/respirationAlpha2DimRange
IRI: https://w3id.org/brainteaser/ontology/schema/respirationAlpha2ExpMean
IRI: https://w3id.org/brainteaser/ontology/schema/respirationAlpha2ExpRange
IRI: https://w3id.org/brainteaser/ontology/schema/respirationApEn
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IRI: https://w3id.org/brainteaser/ontology/schema/respirationDFAalpha2
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IRI: https://w3id.org/brainteaser/ontology/schema/respirationSD2
IRI: https://w3id.org/brainteaser/ontology/schema/respirationSD2SD1
IRI: https://w3id.org/brainteaser/ontology/schema/respirationSDBB
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IRI: https://w3id.org/brainteaser/ontology/schema/respiratorySubscore
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IRI: https://w3id.org/brainteaser/ontology/schema/restStressDuration
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IRI: https://w3id.org/brainteaser/ontology/schema/smokersNumber
IRI: https://w3id.org/brainteaser/ontology/schema/smokingExpositionType
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IRI: https://w3id.org/brainteaser/ontology/schema/spo2AODMax
IRI: https://w3id.org/brainteaser/ontology/schema/spo2AV
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IRI: https://w3id.org/brainteaser/ontology/schema/visualEvokedPotentialLatency
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IRI: https://w3id.org/brainteaser/ontology/schema/whoThresholdExceeded
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The authors would like to thank Silvio Peroni for developing LODE, a Live OWL Documentation Environment, which is used for representing the Cross Referencing Section of this document and Daniel Garijo for developing Widoco, the program used to create the template used in this documentation.