A toolkit for evaluating and monitoring AI models in clinical settings

Unlocking the Power of Health Data With a Modern Data Lakehouse

Streamlined Data Mapping to OMOP

Model Context Protocol Server for the Observational Medical Outcomes Partnership (OMOP) Common Data Model

An ETL pipeline to transform your EMP data to OMOP.

Model Context Protocol (MCP) server for mapping clinical terminology to Observational Medical Outcomes Partnership (OMOP) concepts using Large Language Models

The omop2survey Python package transforms standardized response codes from the OMOP CDM survey variables into numeric values and simplifies data preparation by providing functionalities for mapping and converting response codes, as well as handling missing data, facilitating easier and more reliable data analysis

An automated system for mapping source medical concepts to OMOP standard concepts using vector similarity search and LLM-based reranking.

🦉 An OWL ontology for the OMOP Common Data Model

Get an OMOP CDM database running quickly.

A Python data class representation of the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)

Federated summary algorithm for a RDB following the OMOP CDM and using Vantage6

OMOP Common Data Model.

Streamlined Data Transformation to OMOP

Update Usagi mappings with kotobuki

SQLAlchemy Declarative Mapping Models for OHDSI OMOP CDM

An overview of the OMOP analytics toolings

tool to facilitate the setup and ongoing maintenance of OHDSI & EHDEN tool deployments

Custom Airflow operators and hooks, that help to upload our raw hospital data to Google Cloud.

A tool for backing up OHDSI WebAPI cohorts in a git repository