A toolkit for evaluating and monitoring AI models in clinical settings

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

Unlocking the Power of Health Data With a Modern Data Lakehouse

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

Streamlined Data Mapping to OMOP

An ETL pipeline to transform your EMP data to OMOP.

Get an OMOP CDM database running quickly.

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

Fast Semantic Static Validator

🦉 An OWL ontology for the OMOP Common Data Model

Python SDK for OMOP/OHDSI vocabularies - query 10M+ medical concepts across SNOMED, ICD-10, RxNorm, LOINC & 90+ terminologies via simple API

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

🩺 Analyze post-stroke aphasia risks by investigating medication patterns and mental health impacts to improve patient outcomes and reduce hospital readmissions.

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

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

Update Usagi mappings with kotobuki

Streamlined Data Transformation to OMOP

Turn one plain-English sentence into a validated, audit-ready clinical algorithm — DAG, OMOP terminology anchoring, a certified bundle, and a SQL cost preflight. Open governance tooling for clinical scenarios.

OMOP Common Data Model.

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