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
pip install pyomop
Usage
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
# 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
Troubleshoot
pip install sqlalchemy==1.3.24
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..
Contributors
- Bell Eapen |
- PRs welcome. See CONTRIBUTING.md
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