omop-semantics

omop-semantics is a Python library for defining and managing semantic conventions on top of OMOP CDM.

It gives you a structured, versioned, portable layer for describing which OMOP concepts are valid in a given context, how they map to CDM row shapes, and what fallback concepts to use when a mapping cannot be completed. Conventions are authored in YAML, validated against schemas, and exposed as typed Python objects — not scattered across ETL code, SQL, or documentation.

Three runtime surfaces

Value-set runtime
Stable named concept ids for application logic:
from omop_semantics.runtime.default_valuesets import runtime
Template/profile runtime
Compiled semantic templates and CDM profiles via OmopSemanticEngine.
Fallback concepts
Canonical unknown and default concepts with reason codes:
from omop_semantics.unknowns import UNKNOWN

Key properties

Portable — no database or vocabulary graph required
Versionable — conventions are tracked in git alongside code
Extensible — add project-specific definitions on top of the shipped ones
Integrates with pipelines — drives ETL logic, validation, and documentation from a single source

When to use this

Use omop-semantics if you:

have project-specific rules about which OMOP concepts are valid,
need consistent concept groupings across ETL and analytics,
want semantic conventions to be explicit, testable, and versioned,
are working in domains like oncology where OMOP alone is too permissive.

Docs

Usage — loading paths and code patterns
Data Model — profiles, templates, and semantic objects
Schema & Instances — authoring assets and file organisation
Fallback Concepts — the shipped unknown and default concepts
Internals — package structure and load-time behaviour

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