OMOP CDM utils
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.
- Postgres
- MySQL
- SqLite
- More to follow..
pip install pyomop
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)
pyomop -help
- Integration with machine learning libraries