omop-alchemy 0.6.0

SQLAlchemy-based models, validation, and utilities for the OHDSI OMOP Common Data Model

OMOP Alchemy

OMOP Alchemy provides a canonical, typed, SQLAlchemy-first representation of the OHDSI OMOP Common Data Model (CDM).

It is designed to support research-ready analytics, validation, and exploration of OMOP data using modern Python tooling, without imposing ETL conventions or execution-time side effects.

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Design goals

OMOP Alchemy is intentionally:

• Declarative
  Defines tables, columns, relationships, and constraints

• SQLAlchemy-native
  Built for SQLAlchemy 2.x ORM usage

• Safe to import anywhere
  No implicit engine creation, no global state, no environment assumptions.

• Typed and inspectable
  Models are fully typed and introspectable for validation, tooling, and IDE support.

• Backend-agnostic
  Designed to work across PostgreSQL, SQLite, and other SQLAlchemy-supported databases.

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What this package does not do

OMOP Alchemy deliberately avoids:

• Enforcing ETL conventions or data pipelines
• Auto-creating databases or loading vocabularies
• Imposing analytics frameworks or dashboards
• Making assumptions about deployment environments

These concerns are intentionally left to downstream tooling.

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Core features

• SQLAlchemy ORM models for OMOP CDM tables
• Explicit foreign key and relationship definitions
• Read-only View classes for safe navigation and analytics
• Domain validation helpers for OMOP concept integrity
• CSV loading utilities for controlled ingestion and testing
• Lightweight schema and model validation against CDM specs

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Example (concept navigation)

from omop_alchemy.model.vocabulary import ConceptView

concept = session.get(ConceptView, 320128)  # Lung cancer
concept.domain.domain_id        # "Condition"
concept.vocabulary.vocabulary_id  # "SNOMED"
concept.is_standard             # True

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Status

This project is currently beta.

The API is stabilising, but some modules may change as real-world use cases expand. Feedback and issues are welcome.

Some additional background

This work builds on earlier research and tooling presented at the 2023 OHDSI APAC Symposium

see background paper.