pyomop 3.0.0

OMOP CDM utils in Python

pyomop

OMOP CDM utils. This repo may be similar to @jbadger3's inspectomop, but this is not a fork.

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.

Support

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

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

Contributors

• Bell Eapen