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- Ability to share a conceptual space across an organization (or organizations).
- Ability to allow individual stakeholders to "use their own language" when interacting with data while retaining the ability to have a single conceptual model behind those interactions.
- A special case of this is a multi-lingual thesaurus that supports terms in multiple languages for the same "concept".
- Harmonizing lexicons across organizations
- Only when organizations have formally defined lexicons
- Concept-based indexing and retrieval
- Can be used to support "lay person" searching within an otherwise highly technical content domain (e.g. medicine).
- High level semantic categorization
- e.g. UMLS semantic type, or "top ontology" like BFO
- Semantic-search (requires an ontology, not just a simple taxonomy or other "lighter" structured terminology)
- Ability to ask questions like: "How many patients suffered a disease/disorder whose finding site is in the kidney?"
- Ability to ask questions like: "What are all the prescribable beta blockers (e.g. in the formulary)?"
- Information model binding
- Support expression of data gathered in a way that is bound to a terminology (for later semantic analysis)
- Linked data
- Expressing data gathered in terms of information models bound to a terminology (this is really a precursor to "semantic search").
- Free-text analysis (and concept extraction), Mining of unstructured text
- "Levels of aggregation" analysis
- e.g. "UKTC" categories tool that produces a sub-set of SNOMED where each "leaf" node has about the same number of children. These are analytically derived "categorizations" within the terminology.
- Research analytics
- E.g. “Given two medications for treating the same condition, which one is more effective?”
- Predictive/Trend analytics
- E.g. “how likely is patient X to become obese given family history and outcomes of similar patients?”
- Cross-terminology mapping
- Reporting
- Quality reporting
- Mortality and epidiomology reporting
- Public health surveillance
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