A small container to get an OMOP CDM database running quickly, with support for both PostgreSQL and SQL Server.

Drop your data into data/, and run the container.

You can configure the container or CLI using the following environment variables:

• DB_HOST: The hostname of the database. Default is db.
• DB_PORT: The port number of the database. Default is 5432.
• DB_USER: The username for the database. Default is postgres.
• DB_PASSWORD: The password for the database. Default is password.
• DB_NAME: The name of the database. Default is omop.
• DIALECT: The type of database to use. Default is postgresql, but can also be mssql.
• SCHEMA_NAME: The name of the schema to be created/used in the database. Default is public.
• DATA_DIR: The directory containing the data CSV files. Default is data.
• SYNTHETIC: Load synthetic data (boolean). Default is false
• SYNTHETIC_NUMBER: Size of synthetic data, 100 or 1000. Default is 100.
• DELIMITER: The delimiter used to separate data. Default is tab, can also be ,

pip install omop-lite python omop-lite --help

docker run -v ./data:/data ghcr.io/health-informatics-uon/omop-lite

# docker-compose.yml
services:
  omop-lite:
    image: ghcr.io/health-informatics-uon/omop-lite
    volumes:
      - ./data:/data
    depends_on:
      - db

  db:
    image: postgres:latest
    environment:
      - POSTGRES_DB=omop
      - POSTGRES_PASSWORD=password
    ports:
      - "5432:5432"

To install using Helm:

# Add the Helm repository
helm install omop-lite oci://ghcr.io/health-informatics-uon/charts/omop-lite --version 0.2.2

The Helm chart deploys OMOP Lite as a Kubernetes Job that creates an OMOP CDM in a database. You can customise the installation using a values file:

# values.yaml
env:
  dbHost: postgres
  dbPort: "5432"
  dbUser: postgres
  dbPassword: postgres
  dbName: omop_helm
  dialect: postgresql
  schemaName: public
  synthetic: "false" 

Install with custom values:

helm install omop-lite omop-lite/omop-lite -f values.yaml

If you need synthetic data, some is provided in the synthetic directory. It provides a small amount of data to load quickly. To load the synthetic data, run the container with the SYNTHETIC environment variable set to true.

• 100 is fake data.
• 1000 is Synthea 1k data.

You can provide your own data for loading into the tables by placing your files in the data/ directory. This should contain .csv files matching the data tables (DRUG_STRENGTH.csv, CONCEPT.csv, etc.).

To match the vocabulary files from Athena, this data should be tab-separated, but as a .csv file extension. You can override the delimiter with DELIMITER configuration.

Adding a tsvector column to the concept table and an index on that column makes full-text search queries on the concept table run much faster. This can be configured by setting FTS_CREATE to be non-empty in the environment.

Postgres does vector search too! To enable this on omop-lite, you can compose the compose-omop-ts.yml with

docker compose -f compose-omop-ts.yml

To do this, you need to have embeddings/embeddings.parquet, containing concept_ids and embeddings. This uses pgvector to create an embeddings table.

If you're a developer and want to iterate on omop-lite quickly, there's a small subset of the vocabularies sufficient to build in synthetic/. If you wish to test the vector search, there are matching embeddings in embeddings/embeddings.parquet.