Skip to content

Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries!

License

Notifications You must be signed in to change notification settings

dermatologist/pyomop

Repository files navigation

pyomop

OMOP CDM utils

Description

The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases. This is a python library to use the CDM v6 compliant databases.

Support

  • Postgres
  • MySQL
  • SqLite
  • More to follow..

Installation

pip install pyomop

Usage

from pyomop import CdmEngineFactory, CdmVocabulary, Cohort, Vocabulary, metadata
from sqlalchemy.sql import select
import datetime

cdm = CdmEngineFactory()  # Creates SQLite database by default

engine = cdm.engine
# Create Tables 
metadata.create_all(engine)
# Create vocabulary
vocab = CdmVocabulary(cdm)
# vocab.create_vocab('/path/to/csv/files')  # Uncomment to load vocabulary csv files

# SQLAlchemy as ORM
session =  cdm.session
session.add(Cohort(cohort_definition_id=2, subject_id=100, 
            cohort_end_date=datetime.datetime.now(), 
            cohort_start_date=datetime.datetime.now()))
session.commit()

s = select([Cohort])
result = session.execute(s)
for row in result:
    print(row)
result.close()
for v in session.query(Vocabulary).order_by(Vocabulary.vocabulary_name):
    print(v.vocabulary_name)

command-line usage

pyomop -help

What to expect

  • Integration with machine learning libraries

Contributors