omop-semantics 0.1.9

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omop_semantics

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

It lets you describe conventions in code

• which OMOP concepts you want to have on hand as named key concepts to improve ergonomics in analytic code,
• how they are grouped,
• what roles they play
• and provide profiles to render these targets uniformly into CDM tables.

The goal is to make these conventions explicit, versioned, and reusable, instead of being buried in code, SQL, or documentation. They are also extensible so that you can add opinionated layers on top of default specifications that may be relevant in a domain-specific context only.

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Key ideas

• Human-authored
  Semantic rules and concept groups are written in YAML and validated with schemas.

• Portable
  No database or graph store required.

• Versionable
  Conventions can evolve over time and be tracked in git.

• Integrates with pipelines
  Can drive ETL logic, validation, and documentation so they stay in sync.

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Typical workflow

1. Define a schema
   Describes what kinds of semantic objects and roles exist (e.g. staging, modifiers).

2. Write YAML instances
   Lists actual OMOP concepts and groups used in your project.

3. Load a runtime registry
   This gives you a programmatic API to query concepts, groups, and relationships.

4. Use it in code
   For validation, cohort logic, ETL constraints, or documentation.

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When should you 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.