README.md

pyomop

• Inspired by @jbadger3's inspectomop

Documentation

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 using SQLAlchemy as the ORM. pyomop also supports converting query results to a pandas dataframe (see below) for use in machine learning pipelines. See some useful SQL Queries here.

Installation (stable)

pip install pyomop

Installation (current)

• git clone this repository and:

pip install -e .

Usage >= 4.0.0 (Async) Example

from pyomop import CdmEngineFactory, CdmVocabulary, CdmVector, Cohort, Vocabulary, metadata
from sqlalchemy.future import select
import datetime
import asyncio

async def main():
    cdm = CdmEngineFactory()  # Creates SQLite database by default
    # Postgres example (db='mysql' also supported)
    # cdm = CdmEngineFactory(db='pgsql', host='', port=5432,
    #                       user='', pw='',
    #                       name='', schema='cdm6')

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

    # Add a cohort
    async with cdm.session() as session:
        async with session.begin():
            session.add(Cohort(cohort_definition_id=2, subject_id=100,
                cohort_end_date=datetime.datetime.now(),
                cohort_start_date=datetime.datetime.now()))
        await session.commit()

    # Query the cohort
    stmt = select(Cohort).where(Cohort.subject_id == 100)
    result = await session.execute(stmt)
    for row in result.scalars():
        print(row)
        assert row.subject_id == 100

    # Query the cohort pattern 2
    cohort = await session.get(Cohort, 1)
    print(cohort)
    assert cohort.subject_id == 100

    # Convert result to a pandas dataframe
    vec = CdmVector()
    vec.result = result
    print(vec.df.dtypes)

    result = await vec.sql_df(cdm, 'TEST') # TEST is defined in sqldict.py
    for row in result:
        print(row)

    result = await vec.sql_df(cdm, query='SELECT * from cohort')
    for row in result:
        print(row)


    # Close session
    await session.close()
    await engine.dispose()

# Run the main function
asyncio.run(main())

Usage <=3.2.0

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

cdm = CdmEngineFactory()  # Creates SQLite database by default

# Postgres example (db='mysql' also supported)
# cdm = CdmEngineFactory(db='pgsql', host='', port=5432,
#                       user='', pw='',
#                       name='', schema='cdm6')


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

# Create a Cohort (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()

result = session.query(Cohort).all()
for row in result:
    print(row)

# Convert result to a pandas dataframe
vec = CdmVector()
vec.result = result
print(vec.df.dtypes)

# Execute a query and convert it to dataframe
vec.sql_df(cdm, 'TEST') # TEST is defined in sqldict.py
print(vec.df.dtypes) # vec.df is a pandas dataframe
# OR
vec.sql_df(cdm, query='SELECT * from cohort')
print(vec.df.dtypes) # vec.df is a pandas dataframe

command-line usage

pyomop -help

Other utils

Want to convert FHIR to pandas data frame? Try fhiry

Use the same functions in .NET and Golang!

Support

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

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Contributors

• Bell Eapen |
• PRs welcome. See CONTRIBUTING.md