- Inspired by @jbadger3's inspectomop
Recently added support for LLM based natural language queries of OMOP CDM databases using llama-index. Please install the llm extras from the develop branch as follows. Please be cognizant of the privacy issues with publically hosted LLMs. Any feedback will be highly appreciated. See usage!
git clone https://github.com/dermatologist/pyomop.git@develop
cd pyomop
pip install -e .[llm]
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.
pip install pyomop
- git clone this repository and:
pip install -e .
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())
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
pyomop -help
Want to convert FHIR to pandas data frame? Try fhiry
Use the same functions in .NET and Golang!
- Postgres
- MySQL
- SqLite
- More to follow..
If you find this project useful, give us a star. It helps others discover the project.
- Bell Eapen |
- PRs welcome. See CONTRIBUTING.md
