Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries, MCP server and FHIR import.

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

An ETL pipeline to transform your EMP data to OMOP.

Update Usagi mappings with kotobuki

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

An overview of the OMOP analytics toolings

Python SDK for OMOP/OHDSI vocabularies - query 9M+ medical concepts across SNOMED, ICD-10, RxNorm, LOINC & 90+ terminologies via simple API

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

An analysis pipeline using OHDSI/OMOP Pharmetrics+ data to evaluate potentially inappropriate medications, mental health comorbidities, and polypharmacy in post-stroke aphasia patients. Includes cohort construction, EDA, flagging tables, and predictive/ explanatory models to evaluate medication-related risks and hospital readmission patterns.

SQLAlchemy Declarative Mapping Models for OHDSI OMOP CDM

Deletion of unwanted content from OMOP CDM

A robust, evidence-based pipeline for generating a high-quality mapping between OHDSI and UMLS concepts using DuckDB and a hierarchical, resumable approach.

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

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

OHDSI Vocabulary to RDF converter