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Drivers for the development of an animal health surveillance ontology
Crawford Revie Fernanda Dórea Ann Lindberg Flavie Vial Karl Hammar Eva Blomqvist
International Conference of Animal Health Surveillance (ICAHS), 2017
Drivers for the development of an animal health surveillance - - PowerPoint PPT Presentation
Drivers for the development of an animal health surveillance - - PowerPoint PPT Presentation
Drivers for the development of an animal health surveillance ontology Fernanda Drea Karl Hammar Ann Lindberg Flavie Vial Crawford Revie Eva Blomqvist International Conference of Animal Health Surveillance (ICAHS), 2017 An ontology defines
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- Different
dimensions of knowledge contained in the data VeNom (Veterinary Nomeclature)
'Squamous cell carcinoma - clitoral' 'Squamous cell carcinoma - conjunctival' 'Squamous cell carcinoma - corneal' 'Squamous cell carcinoma - gastric (stomach)' 'Squamous cell carcinoma - penis/prepuce' 'Squamous cell carcinoma - oesophageal' 'Squamous cell carcinoma - nasal sinus' 'Squamous cell carcinoma - perineal' 'Squamous cell carcinoma - third eyelid/nictitating membrane' 'Squamous cell carcinoma - urethral' 'Squamous cell carcinoma - urinary bladder'
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- Different
dimensions of knowledge contained in the data MeSH Terms
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- Different
dimensions of knowledge contained in the data
Wine
Red White Rose
Wine
Sparkling Non- sparkling
Wine
Red White Rose
Sparkling Non- sparkling Sparkling Non- sparkling Sparkling Non- sparkling
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Ontologies
- Data model
- Classes
- Properties
- Instances
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Why use ontologies?
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To share common understanding of the structure of information among people or software agents
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To enable reuse of domain knowledge
Uberon multi-species anatomy
- ntology
Anatomical Entity Ontology Foundational Model of Anatomy Ontology for General Medical Science Symptom Ontology Clinical Measurement Ontology GO Gene Ontology
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To re-use domain independent knowledge
FOAF (‘people’) Ontology SKOS (‘Thesuaral’ structure) Ontology Geonames (‘GIS’) Ontology
schema.org
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To make domain assumptions explicit
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To support research and knowledge discovery from data
Fracture of the femur Osteochondroma of femur All injuries of the femur? All injuries of the LEG?
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Ontologies applied to data-driven surveillance
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Desired functions
- Convert health data into information in real-time
- Use medical knowledge to infer surveillance relevant
information from data collected for other purposes
- Provide a permenant source of term mappings that are
- pen and can be shared/expanded by community (IRI)
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Inherent challenges to overcome
- Distributed data (not likely to be shared)
- Data non-coded or coded using different standards
- Solutions must work prospectively and retrospectively
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Sustainability of solutions
- Maintenance
- Reviews and updates
- Scalability
- Transparency
- Interoperability
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Module 2 – clinical data Module 1 – animal registry Module 3 – laboratory data Abattoir data
VeNom
https://w3id.org/ahso
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Workflow for each data source Concepts Data model Fill the gaps
Improve / expand
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Community involvement
- Workgroups for each module/data type
- Review outputs and submit issues
- Google forum
- Github
- Home page
- Open edit book
datadrivensurveillance.org/ahso
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Challenge to ‘big data’ epi teams
- microdata
- JSON-LD
- schema.org
- RDF
- OWL
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Just when you thought it was safe to be a quantitative epidemologist
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